Developmental dissociation of analytical and holistic object recognition in adolescence

Previous research (e.g., Jüttner et al, 2013, Developmental Psychology, 49, 161-176) has shown that object recognition may develop well into late childhood and adolescence. The present study extends that research and reveals novel di erences in holistic and analytic recognition performance in 7-11 year olds compared to that seen in adults. We interpret our data within Hummel’s hybrid model of object recognition (Hummel, 2001, Visual Cognition, 8, 489-517) that proposes two parallel routes for recognition (analytic vs. holistic) modulated by attention. Using a repetition-priming paradigm, we found in Experiment 1 that children showed no holistic priming, but only analytic priming. Given that holistic priming might be thought to be more ‘primitive’, we confirmed in Experiment 2 that our surprising finding was not because children’s analytic recognition was merely a result of name repetition. Our results suggest a developmental primacy of analytic object recognition. By contrast, holistic object recognition skills appear to emerge with a much more protracted trajectory extending into late adolescence

Developmental commonalities between object and face recognition in adolescence

In the visual perception literature, the recognition of faces has often been contrasted with that of non-face objects, in terms of differences with regard to the role of parts, part relations and holistic processing. However, recent evidence from developmental studies has begun to blur this sharp distinction. We review evidence for a protracted development of object recognition that is reminiscent of the well-documented slow maturation observed for faces. The prolonged development manifests itself in a retarded processing of metric part relations as opposed to that of individual parts and offers surprising parallels to developmental accounts of face recognition, even though the interpretation of the data is less clear with regard to holistic processing. We conclude that such results might indicate functional commonalities between the mechanisms underlying the recognition of faces and non-face objects, which are modulated by different task requirements in the two stimulus domains.

Neural activity during object perception in schizophrenia patients is associated with illness duration and affective symptoms

Background - Abnormalities in visual processes have been observed in schizophrenia patients and have been associated with alteration of the lateral occipital complex and visual cortex. However, the relationship of these abnormalities with clinical symptomatology is largely unknown. Methods - We investigated the brain activity associated with object perception in schizophrenia. Pictures of common objects were presented to 26 healthy participants (age = 36.9; 11 females) and 20 schizophrenia patients (age = 39.9; 8 females) in an fMRI study. Results - In the healthy sample the presentation of pictures yielded significant activation (pFWE (cluster) < 0.001) of the bilateral fusiform gyrus, bilateral lingual gyrus, and bilateral middle occipital gyrus. In patients, the bilateral fusiform gyrus and bilateral lingual gyrus were significantly activated (pFWE (cluster) < 0.001), but not so the middle occipital gyrus. However, significant bilateral activation of the middle occipital gyrus (pFWE (cluster) < 0.05) was revealed when illness duration was controlled for. Depression was significantly associated with increased activation, and anxiety with decreased activation, of the right middle occipital gyrus and several other brain areas in the patient group. No association with positive or negative symptoms was revealed. Conclusions - Illness duration accounts for the weak activation of the middle occipital gyrus in patients during picture presentation. Affective symptoms, but not positive or negative symptoms, influence the activation of the right middle occipital gyrus and other brain areas.

Visual signs and symptoms of corticobasal degeneration

Corticobasal degeneration is a rare, progressive neurodegenerative disease and a member of the 'parkinsonian' group of disorders, which also includes Parkinson's disease, progressive supranuclear palsy, dementia with Lewy bodies and multiple system atrophy. The most common initial symptom is limb clumsiness, usually affecting one side of the body, with or without accompanying rigidity or tremor. Subsequently, the disease affects gait and there is a slow progression to influence ipsilateral arms and legs. Apraxia and dementia are the most common cortical signs. Corticobasal degeneration can be difficult to distinguish from other parkinsonian syndromes but if ocular signs and symptoms are present, they may aid clinical diagnosis. Typical ocular features include increased latency of saccadic eye movements ipsilateral to the side exhibiting apraxia, impaired smooth pursuit movements and visuo-spatial dysfunction, especially involving spatial rather than object-based tasks. Less typical features include reduction in saccadic velocity, vertical gaze palsy, visual hallucinations, sleep disturbance and an impaired electroretinogram. Aspects of primary vision such as visual acuity and colour vision are usually unaffected. Management of the condition to deal with problems of walking, movement, daily tasks and speech problems is an important aspect of the disease.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Exploring the World's Visual History

Thesis (Ph.D.)--University of Washington, 2016-08

Relocating the Frame: Object-Oriented (1.) and Locative Art (2.) Environments

The locative project is in a condition of emergence, an embryonic state in which everything is still up for grabs, a zone of consistency yet to emerge. As an emergent practice locative art, like locative media generally, it is simultaneously opening up new ways of engaging in the world and mapping its own domain. (Drew Hemment, 2004) Artists and scientists have always used whatever emerging technologies existed at their particular time throughout history to push the boundaries of their fields of practice. The use of new technologies or the notion of ‘new’ media is neither particularly new nor novel. Humans are adaptive, evolving and will continue to invent and explore technological innovation. This paper asks the following questions: what role does adaptive and/or intelligent art play in the future of public spaces, and how does this intervention alter the relationship between theory and practice? Does locative or installation-based art reach more people, and does ‘intelligent’ or ‘smart’ art have a larger role to play in the beginning of this century? The speakers will discuss their current collaborative prototype and within the presentation demonstrate how software art has the potential to activate public spaces, and therefore contribute to a change in spatial or locative awareness. It is argued that the role and perhaps even the representation of the audience/viewer is left altered through this intervention. 1. A form of electronic imagery created by a collection of mathematically defined lines and/or curves. 2. An experiential form of art which engages the viewer both from within a specific location and in response to their intentional or unintentional input.

RICH AND EFFICIENT VISUAL DATA REPRESENTATION

Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

O museu virtual Google Art Project : um estudo de caso em contexto de ensino-aprendizagem da educação visual

A educação na arte e pela arte confere a todos os seus intervenientes a estimulação da sua criatividade e da sua consciência cultural, proporcionando meios para se exprimirem e participarem ativamente no mundo que nos rodeia. A integração das tecnologias de informação e comunicação no processo de ensino-aprendizagem veio alargar o papel que a arte pode desempenhar neste processo, promovendo novas formas de aprender, de ensinar e de pensar. Assim, a utilização de ambientes virtuais em contexto educativo tem revelado um enorme potencial, sobretudo ao nível da comunicação e da interação entre alunos e obras de arte. Neste sentido, considerou-se importante desenvolver um estudo de caso em contexto de sala de aula da Educação Visual, promovendo uma aprendizagem baseada na articulação entre a observação, interpretação e análise da obra de arte e o museu virtual. Assim o principal objetivo deste estudo foi avaliar as potencialidades do Google Art Project, enquanto objeto de aprendizagem, na promoção da aprendizagem na área da literacia em artes. Para além disso, procurámos ainda avaliar se a utilização de ferramentas multimédia como o referido Google Art Project e o Quadro Interativo, constituem fatores de motivação na aprendizagem da disciplina de Educação Visual. Do ponto de vista metodológico desenvolvemos uma estratégia baseada na investigação-ação. Procurámos, por um lado, descobrir e compreender o significado de uma realidade vivida por um grupo de alunos e, por outro lado, refletir sobre a prática educativa com o intuito de a melhorar e transformar. Este estudo envolveu cinco turmas do sexto ano do ensino público. Para a recolha de dados utilizámos técnicas baseadas na conversação e na observação, no questionário e nas notas de campo. Os resultados deste estudo revelam que as ferramentas tecnológicas utilizadas podem efetivamente contribuir para a promoção da aprendizagem dos alunos na área da Educação Visual, mais concretamente ao nível do domínio da literacia artística, da representação e da interpretação visual.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

The neuroscience of cognitive enhancement : enhanced attention, working memory and visual information processing speed using 3D-MOT

Des interventions ciblant l’amélioration cognitive sont de plus en plus à l’intérêt dans nombreux domaines, y compris la neuropsychologie. Bien qu'il existe de nombreuses méthodes pour maximiser le potentiel cognitif de quelqu’un, ils sont rarement appuyé par la recherche scientifique. D’abord, ce mémoire examine brièvement l'état des interventions d'amélioration cognitives. Il décrit premièrement les faiblesses observées dans ces pratiques et par conséquent il établit un modèle standard contre lequel on pourrait et devrait évaluer les diverses techniques ciblant l'amélioration cognitive. Une étude de recherche est ensuite présenté qui considère un nouvel outil de l'amélioration cognitive, une tâche d’entrainement perceptivo-cognitive : 3-dimensional multiple object tracking (3D-MOT). Il examine les preuves actuelles pour le 3D-MOT auprès du modèle standard proposé. Les résultats de ce projet démontrent de l’augmentation dans les capacités d’attention, de mémoire de travail visuel et de vitesse de traitement d’information. Cette étude représente la première étape dans la démarche vers l’établissement du 3D-MOT comme un outil d’amélioration cognitive.