Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance. 

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Detection of unusual mutation within the VP1 region of different re-isolates of poliovirus Sabin vaccine

In the present study, a genomic analysis of full VP1 sequence region of 15 clinical re-isolates (14 healthy vaccinees and one bone marrow tumor patient) was conducted, aiming to the identification of mutations and to the assessment of their impact on virus fitness, providing also insights relevant with the natural evolution of Sabin strains. Clinical re-isolates were analyzed by RT-PCR, sequencing and computational analysis. Some re-isolates were characterized by an unusual mutational pattern in which non-synonymous mutations outnumbered the synonymous ones. Furthermore, the majority of amino-acid substitutions were located in the capsid exterior, specifically in N-Ags, near N-Ags and in the north rim of the canyon. Also mutations, which are well-known determinants of attenuation, were identified. The results of this study propose that some re-isolates are characterized by an evolutionary pattern in which non-synonymous mutations with a direct phenotypic impact on viral fitness are fixed in viral genomes, in spite of synonymous ones with no phenotypic impact on viral fitness. Results of the present retrospective characterization of Sabin clinical re-isolates, based on the full VP1 sequence, suggest that vaccine-derived viruses may make their way through narrow breaches and may evolve into transmissible pathogens even in adequately immunized populations. For this reason increased poliovirus laboratory surveillance should be permanent and full VP1 sequence analysis should be conducted even in isolates originating from healthy vaccinees.

An enhanced fall detection system for elderly person monitoring using consumer home networks

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate.

A supervised cooperative learning system for early detection of language disorders

The Quality of Life of a person may depend on early attention to his neurodevel-opment disorders in childhood. Identification of language disorders under the age of six years old can speed up required diagnosis and/or treatment processes. This paper details the enhancement of a Clinical Decision Support System (CDSS) aimed to assist pediatricians and language therapists at early identification and re-ferral of language disorders. The system helps to fine tune the Knowledge Base of Language Delays (KBLD) that was already developed and validated in clinical routine with 146 children. Medical experts supported the construction of Gades CDSS by getting scientific consensus from literature and fifteen years of regis-tered use cases of children with language disorders. The current research focuses on an innovative cooperative model that allows the evolution of the KBLD of Gades through the supervised evaluation of the CDSS learnings with experts¿ feedback. The deployment of the resulting system is being assessed under a mul-tidisciplinary team of seven experts from the fields of speech therapist, neonatol-ogy, pediatrics, and neurology.

Hand detection on images based on deformable part models and additional features

Hand detection on images has important applications on person activities recognition. This thesis focuses on PASCAL Visual Object Classes (VOC) system for hand detection. VOC has become a popular system for object detection, based on twenty common objects, and has been released with a successful deformable parts model in VOC2007. A hand detection on an image is made when the system gets a bounding box which overlaps with at least 50% of any ground truth bounding box for a hand on the image. The initial average precision of this detector is around 0.215 compared with a state-of-art of 0.104; however, color and frequency features for detected bounding boxes contain important information for re-scoring, and the average precision can be improved to 0.218 with these features. Results show that these features help on getting higher precision for low recall, even though the average precision is similar.

Detection of metallo-'beta'-lactamases-encoding genes in environmental isolates of Aeromonas hydrophila and Aeromonas jandaei

Aims: To determine the prevalence and expression of metallo-beta-lactamases (MBL)-encoding genes in Aeromonas species recovered from natural water reservoirs in southeastern Brazil. Methods and Results: Eighty-seven Aeromonas isolates belonging to Aeromonas hydrophila (n = 41) and Aer. jandaei (n = 46) species were tested for MBL production by the combined disk test using imipenem and meropenem disks as substrates and EDTA or thioglycolic acid as inhibitors. The presence of MBL genes was investigated by PCR and sequencing using new consensus primer pairs designed in this study. The cphA gene was found in 97.6% and 100% of Aer. hydrophila and Aer. jandaei isolates, respectively, whereas the acquired MBL genes bla(IMP), bla(VIM) and bla(SPM-1) were not detected. On the other hand, production of MBL activity was detectable in 87.8% and 10.9% of the cphA-positive Aer. hydrophila and Aer. jandaei isolates respectively. Conclusions: Our results indicate that cphA seems to be intrinsic in the environmental isolates of Aer. hydrophila and Aer. jandaei in southeastern Brazil, although, based on the combined disk test, not all of them are apparently able to express the enzymatic activity. Significance and Impact of the Study: These data confirm the presence of MBL-producing Aeromonas species in natural water reservoirs. Risk of water-borne diseases owing to domestic and industrial uses of freshwater should be re-examined from the increase of bacterial resistance point of view

Immunohistochemical detection of polyductin and co-localization with liver progenitor cell markers during normal and abnormal development of the intrahepatic biliary system and in adult hepatobiliary carcinomas

The longest open reading frame of PKHD1 (polycystic kidney and hepatic disease 1), the autosomal recessive polycystic kidney disease (ARPKD) gene, encodes a single-pass, integral membrane protein named polyductin or fibrocystin. A fusion protein comprising its intracellular C-terminus, FP2, was previously used to raise a polyclonal antiserum shown to detect polyductin in several human tissues, including liver. In the current study, we aimed to investigate by immunohistochemistry the detailed polyductin localization pattern in normal (ductal plate [DP], remodelling ductal plate [RDP], remodelled bile ducts) and abnormal development of the primitive intrahepatic biliary system, known as ductal plate malformation (DPM). This work also included the characterization of polyductin expression profile in various histological forms of neonatal and infantile cholestasis, and in cholangiocellular carcinoma (CCC) and hepatocellular carcinoma (HCC). We detected polyductin expression in the intrahepatic biliary system during the DP and the RDP stages as well as in DPM. No specific staining was found at the stage of remodelled bile ducts. Polyductin was also detected in liver biopsies with neonatal cholestasis, including mainly biliary atresia and neonatal hepatitis with ductular reaction as well as congenital hepatic fibrosis. In addition, polyductin was present in CCC, whereas it was absent in HCC. Polyductin was also co-localized in some DP cells together with oval stem cell markers. These results represent the first systematic study of polyductin expression in human pathologies associated with abnormal development of intrahepatic biliary tree, and support the following conclusions: (i) polyductin expression mirrors developmental properties of the primitive intrahepatic biliary system; (ii) polyductin is re-expressed in pathological conditions associated with DPM and (iii) polyductin might be a potential marker to distinguish CCC from HCC.

A combined study of behavior and Fos expression in limbic structures after re-testing Wistar rats in the elevated plus-maze

The elevated plus-maze is an animal model used to study anxiety. In a second session, rats show a reduction in the exploratory behavior even when the two sessions are separated by intervals as large as 7 days. The aim of the present study was to investigate whether the reduction in the exploratory behavior is maintained after intervals larger than 7 days. Additionally, we aimed at investigating eventual correlations between behaviors in the plus-maze and activation of limbic structures as measured by Fos protein expression after the second session. Rats were tested for 5 min in the elevated plus-maze and re-tested 3, 9 or 33 days later. Other groups were tested only once. The rat brains were processed for immunohistochemical detection of Fos protein. The results show a decrease in the open arms exploration in the second trial with intervals of 3, 9 and 33 days. The expression of Fos protein in the piriform cortex, septal nucleus and paraventricular hypothalamic nucleus in the groups tested with intervals of 9 and 33 days were statistically different from the other groups. The alterations observed in exploratory behavior in the second session in the plus-maze did not correlate with Fos expression. In conclusion, although the specific test conditions were sufficient to evoke behavioral alterations in exploration in the elevated plus-maze, they were enough to induce significant Fos protein expression in piriform cortex, septal nucleus and thalamic and hypothalamic paraventricular nuclei but not in other areas such as dorsomedial nucleus of the hypothalamus and amygdala nuclei, known to be also active participants in circuits controlling fear and anxiety. (C) 2010 Elsevier Inc. All rights reserved.

Towards the detection of deception in interactive multimedia environments

A classical application of biosignal analysis has been the psychophysiological detection of deception, also known as the polygraph test, which is currently a part of standard practices of law enforcement agencies and several other institutions worldwide. Although its validity is far from gathering consensus, the underlying psychophysiological principles are still an interesting add-on for more informal applications. In this paper we present an experimental off-the-person hardware setup, propose a set of feature extraction criteria and provide a comparison of two classification approaches, targeting the detection of deception in the context of a role-playing interactive multimedia environment. Our work is primarily targeted at recreational use in the context of a science exhibition, where the main goal is to present basic concepts related with knowledge discovery, biosignal analysis and psychophysiology in an educational way, using techniques that are simple enough to be understood by children of different ages. Nonetheless, this setting will also allow us to build a significant data corpus, annotated with ground-truth information, and collected with non-intrusive sensors, enabling more advanced research on the topic. Experimental results have shown interesting findings and provided useful guidelines for future work. Pattern Recognition

Detection of quasi-periodic processes in complex systems: how do we quantitatively describe their properties?

It has been shown that in reality at least two general scenarios of data structuring are possible: (a) a self-similar (SS) scenario when the measured data form an SS structure and (b) a quasi-periodic (QP) scenario when the repeated (strongly correlated) data form random sequences that are almost periodic with respect to each other. In the second case it becomes possible to describe their behavior and express a part of their randomness quantitatively in terms of the deterministic amplitude–frequency response belonging to the generalized Prony spectrum. This possibility allows us to re-examine the conventional concept of measurements and opens a new way for the description of a wide set of different data. In particular, it concerns different complex systems when the ‘best-fit’ model pretending to be the description of the data measured is absent but the barest necessity of description of these data in terms of the reduced number of quantitative parameters exists. The possibilities of the proposed approach and detection algorithm of the QP processes were demonstrated on actual data: spectroscopic data recorded for pure water and acoustic data for a test hole. The suggested methodology allows revising the accepted classification of different incommensurable and self-affine spatial structures and finding accurate interpretation of the generalized Prony spectroscopy that includes the Fourier spectroscopy as a partial case.