Machine vision to identify broiler breeder behavior

Animal behavioral parameters can be used to assess welfare status in commercial broiler breeders. Behavioral parameters can be monitored with a variety of sensing devices, for instance, the use of video cameras allows comprehensive assessment of animal behavioral expressions. Nevertheless, the development of efficient methods and algorithms to continuously identify and differentiate animal behavior patterns is needed. The objective this study was to provide a methodology to identify hen white broiler breeder behavior using combined techniques of image processing and computer vision. These techniques were applied to differentiate body shapes from a sequence of frames as the birds expressed their behaviors. The method was comprised of four stages: (1) identification of body positions and their relationship with typical behaviors. For this stage, the number of frames required to identify each behavior was determined; (2) collection of image samples, with the isolation of the birds that expressed a behavior of interest; (3) image processing and analysis using a filter developed to separate white birds from the dark background; and finally (4) construction and validation of a behavioral classification tree, using the software tool Weka (model 148). The constructed tree was structured in 8 levels and 27 leaves, and it was validated using two modes: the set training mode with an overall rate of success of 96.7%, and the cross validation mode with an overall rate of success of 70.3%. The results presented here confirmed the feasibility of the method developed to identify white broiler breeder behavior for a particular group of study. Nevertheless, more improvements in the method can be made in order to increase the validation overall rate of success. (C) 2013 Elsevier B.V. All rights reserved.

Static Video Summarization through Optimum-Path Forest Clustering

This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.

HTS and HTSn: New shape descriptors based on Hough transform statistics

With the widespread proliferation of computers, many human activities entail the use of automatic image analysis. The basic features used for image analysis include color, texture, and shape. In this paper, we propose a new shape description method, called Hough Transform Statistics (HTS), which uses statistics from the Hough space to characterize the shape of objects or regions in digital images. A modified version of this method, called Hough Transform Statistics neighborhood (HTSn), is also presented. Experiments carried out on three popular public image databases showed that the HTS and HTSn descriptors are robust, since they presented precision-recall results much better than several other well-known shape description methods. When compared to Beam Angle Statistics (BAS) method, a shape description method that inspired their development, both the HTS and the HTSn methods presented inferior results regarding the precision-recall criterion, but superior results in the processing time and multiscale separability criteria. The linear complexity of the HTS and the HTSn algorithms, in contrast to BAS, make them more appropriate for shape analysis in high-resolution image retrieval tasks when very large databases are used, which are very common nowadays. (C) 2014 Elsevier Inc. All rights reserved.

Swarm-based descriptor combination and its application for image classification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.

Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks

Dynamic texture is a recent field of investigation that has received growing attention from computer vision community in the last years. These patterns are moving texture in which the concept of selfsimilarity for static textures is extended to the spatiotemporal domain. In this paper, we propose a novel approach for dynamic texture representation, that can be used for both texture analysis and segmentation. In this method, deterministic partially self-avoiding walks are performed in three orthogonal planes of the video in order to combine appearance and motion features. We validate our method on three applications of dynamic texture that present interesting challenges: recognition, clustering and segmentation. Experimental results on these applications indicate that the proposed method improves the dynamic texture representation compared to the state of the art.

Prototipo de herramienta para el inventariado automático de piezas de Lego basado en visión artificial

Permitida la difusión del código bajo los términos de la licencia BSD de tres cláusulas.

Gender classification in large databases

[EN]In this paper, we address the challenge of gender classi - cation using large databases of images with two goals. The rst objective is to evaluate whether the error rate decreases compared to smaller databases. The second goal is to determine if the classi er that provides the best classi cation rate for one database, improves the classi cation results for other databases, that is, the cross-database performance.

Improving gender classification accuracy in the wild

[EN]In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Diff erent descriptors, resolutions and classfii ers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

On using periocular biometric for gender classification in the wild

[EN]Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem.

Exploring the use of local binary patterns as focus measure

[EN]In this work local binary patterns based focus measures are presented. Local binary patterns (LBP) have been introduced in computer vision tasks like texture classification or face recognition. In applications where recognition is based on LBP, a computational saving can be achieved with the use of LBP in the focus measures. The behavior of the proposed measures is studied to test if they fulfill the properties of the focus measures and then a comparison with some well know focus measures is carried out in different scenarios.

Riconoscimento di gesti: estensione a movimenti fini su spazi limitati. Teoria e implementazione

Riconoscere un gesto, tracciarlo ed identificarlo è una operazione complessa ed articolata. Negli ultimi anni, con l’avvento massivo di interfacce interattive sempre più sofisticate, si sono ampliati gli approcci nell’interazione tra uomo e macchina. L’obiettivo comune, è quello di avere una comunicazione “trasparente” tra l’utente e il computer, il quale, deve interpretare gesti umani tramite algoritmi matematici. Il riconoscimento di gesti è un modo per iniziare a comprendere il linguaggio del corpo umano da parte della macchina. Questa disciplina, studia nuovi modi di interazione tra questi due elementi e si compone di due macro obiettivi : (a) tracciare i movimenti di un particolare arto; (b) riconoscere tale tracciato come un gesto identificativo. Ognuno di questi due punti, racchiude in sé moltissimi ambiti di ricerca perché moltissimi sono gli approcci proposti negli anni. Non si tratta di semplice cattura dell’immagine, è necessario creare un supporto, a volte molto articolato, nel quale i dati grezzi provenienti dalla fotocamera, necessitano di filtraggi avanzati e trattamenti algoritmici, in modo tale da trasformare informazioni grezze, in dati utilizzabili ed affidabili. La tecnologia riguardo la gesture recognition è rilevante come l’introduzione delle interfacce tattili sui telefoni intelligenti. L’industria oggi ha iniziato a produrre dispositivi in grado di offrire una nuova esperienza, la più naturale possibile, agli utenti. Dal videogioco, all’esperienza televisiva gestita con dei piccoli gesti, all’ambito biomedicale, si sta introducendo una nuova generazione di dispositivi i cui impieghi sono innumerevoli e, per ogni ambito applicativo, è necessario studiare al meglio le peculiarità, in modo tale da produrre un qualcosa di nuovo ed efficace. Questo lavoro di tesi ha l’obiettivo di apportare un contributo a questa disciplina. Ad oggi, moltissime applicazioni e dispositivi associati, si pongono l’obiettivo di catturare movimenti ampi: il gesto viene eseguito con la maggior parte del corpo e occupa una posizione spaziale rilevante. Questa tesi vuole proporre invece un approccio, nel quale i movimenti da seguire e riconoscere sono fatti “nel piccolo”. Si avrà a che fare con gesti classificati fini, dove i movimenti delle mani sono compiuti davanti al corpo, nella zona del torace, ad esempio. Gli ambiti applicativi sono molti, in questo lavoro si è scelto ed adottato l’ambito artigianale.

Riconoscimento di gesti: estensione a più punti del corpo e ad attori multipli

Negli ultimi anni si è assistito ad una radicale rivoluzione nell’ambito dei dispositivi di interazione uomo-macchina. Da dispositivi tradizionali come il mouse o la tastiera si è passati allo sviluppo di nuovi sistemi capaci di riconoscere i movimenti compiuti dall’utente (interfacce basate sulla visione o sull’uso di accelerometri) o rilevare il contatto (interfacce di tipo touch). Questi sistemi sono nati con lo scopo di fornire maggiore naturalezza alla comunicazione uomo-macchina. Le nuove interfacce sono molto più espressive di quelle tradizionali poiché sfruttano le capacità di comunicazione naturali degli utenti, su tutte il linguaggio gestuale. Essere in grado di riconoscere gli esseri umani, in termini delle azioni che stanno svolgendo o delle posture che stanno assumendo, apre le porte a una serie vastissima di interessanti applicazioni. Ad oggi sistemi di riconoscimento delle parti del corpo umano e dei gesti sono ampiamente utilizzati in diversi ambiti, come l’interpretazione del linguaggio dei segni, in robotica per l’assistenza sociale, per indica- re direzioni attraverso il puntamento, nel riconoscimento di gesti facciali [1], interfacce naturali per computer (valida alternativa a mouse e tastiera), ampliare e rendere unica l’esperienza dei videogiochi (ad esempio Microsoft 1 Introduzione Kinect© e Nintendo Wii©), nell’affective computing1 . Mostre pubbliche e musei non fanno eccezione, assumendo un ruolo cen- trale nel coadiuvare una tecnologia prettamente volta all’intrattenimento con la cultura (e l’istruzione). In questo scenario, un sistema HCI deve cercare di coinvolgere un pubblico molto eterogeneo, composto, anche, da chi non ha a che fare ogni giorno con interfacce di questo tipo (o semplicemente con un computer), ma curioso e desideroso di beneficiare del sistema. Inoltre, si deve tenere conto che un ambiente museale presenta dei requisiti e alcune caratteristiche distintive che non possono essere ignorati. La tecnologia immersa in un contesto tale deve rispettare determinati vincoli, come: - non può essere invasiva; - deve essere coinvolgente, senza mettere in secondo piano gli artefatti; - deve essere flessibile; - richiedere il minor uso (o meglio, la totale assenza) di dispositivi hardware. In questa tesi, considerando le premesse sopracitate, si presenta una sistema che può essere utilizzato efficacemente in un contesto museale, o in un ambiente che richieda soluzioni non invasive. Il metodo proposto, utilizzando solo una webcam e nessun altro dispositivo personalizzato o specifico, permette di implementare i servizi di: (a) rilevamento e (b) monitoraggio dei visitatori, (c) riconoscimento delle azioni.

Role of nitric oxide and endocannabinoids in synaptic plasticity in the perirhinal cortex and in visual recognition memory

Introduction and aims of the research Nitric oxide (NO) and endocannabinoids (eCBs) are major retrograde messengers, involved in synaptic plasticity (long-term potentiation, LTP, and long-term depression, LTD) in many brain areas (including hippocampus and neocortex), as well as in learning and memory processes. NO is synthesized by NO synthase (NOS) in response to increased cytosolic Ca2+ and mainly exerts its functions through soluble guanylate cyclase (sGC) and cGMP production. The main target of cGMP is the cGMP-dependent protein kinase (PKG). Activity-dependent release of eCBs in the CNS leads to the activation of the Gαi/o-coupled cannabinoid receptor 1 (CB1) at both glutamatergic and inhibitory synapses. The perirhinal cortex (Prh) is a multimodal associative cortex of the temporal lobe, critically involved in visual recognition memory. LTD is proposed to be the cellular correlate underlying this form of memory. Cholinergic neurotransmission has been shown to play a critical role in both visual recognition memory and LTD in Prh. Moreover, visual recognition memory is one of the main cognitive functions impaired in the early stages of Alzheimer’s disease. The main aim of my research was to investigate the role of NO and ECBs in synaptic plasticity in rat Prh and in visual recognition memory. Part of this research was dedicated to the study of synaptic transmission and plasticity in a murine model (Tg2576) of Alzheimer’s disease. Methods Field potential recordings. Extracellular field potential recordings were carried out in horizontal Prh slices from Sprague-Dawley or Dark Agouti juvenile (p21-35) rats. LTD was induced with a single train of 3000 pulses delivered at 5 Hz (10 min), or via bath application of carbachol (Cch; 50 μM) for 10 min. LTP was induced by theta-burst stimulation (TBS). In addition, input/output curves and 5Hz-LTD were carried out in Prh slices from 3 month-old Tg2576 mice and littermate controls. Behavioural experiments. The spontaneous novel object exploration task was performed in intra-Prh bilaterally cannulated adult Dark Agouti rats. Drugs or vehicle (saline) were directly infused into the Prh 15 min before training to verify the role of nNOS and CB1 in visual recognition memory acquisition. Object recognition memory was tested at 20 min and 24h after the end of the training phase. Results Electrophysiological experiments in Prh slices from juvenile rats showed that 5Hz-LTD is due to the activation of the NOS/sGC/PKG pathway, whereas Cch-LTD relies on NOS/sGC but not PKG activation. By contrast, NO does not appear to be involved in LTP in this preparation. Furthermore, I found that eCBs are involved in LTP induction, but not in basal synaptic transmission, 5Hz-LTD and Cch-LTD. Behavioural experiments demonstrated that the blockade of nNOS impairs rat visual recognition memory tested at 24 hours, but not at 20 min; however, the blockade of CB1 did not affect visual recognition memory acquisition tested at both time points specified. In three month-old Tg2576 mice, deficits in basal synaptic transmission and 5Hz-LTD were observed compared to littermate controls. Conclusions The results obtained in Prh slices from juvenile rats indicate that NO and CB1 play a role in the induction of LTD and LTP, respectively. These results are confirmed by the observation that nNOS, but not CB1, is involved in visual recognition memory acquisition. The preliminary results obtained in the murine model of Alzheimer’s disease indicate that deficits in synaptic transmission and plasticity occur very early in Prh; further investigations are required to characterize the molecular mechanisms underlying these deficits.