Automated Video Analysis of Animal Movements Using Gabor Orientation Filters

To quantify locomotory behavior, tools for determining the location and shape of an animal’s body are a first requirement. Video recording is a convenient technology to store raw movement data, but extracting body coordinates from video recordings is a nontrivial task. The algorithm described in this paper solves this task for videos of leeches or other quasi-linear animals in a manner inspired by the mammalian visual processing system: the video frames are fed through a bank of Gabor filters, which locally detect segments of the animal at a particular orientation. The algorithm assumes that the image location with maximal filter output lies on the animal’s body and traces its shape out in both directions from there. The algorithm successfully extracted location and shape information from video clips of swimming leeches, as well as from still photographs of swimming and crawling snakes. A Matlab implementation with a graphical user interface is available online, and should make this algorithm conveniently usable in many other contexts

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Foveated object recognition by corner search

textHere we describe a gray scale object recognition system based on foveated corner finding, the computation of sequential fixation points, and elements of Lowe’s SIFT transform. The system achieves rotational, transformational, and limited scale invariant object recognition that produces recognition decisions using data extracted from sequential fixation points. It is broken into two logical steps. The first is to develop principles of foveated visual search and automated fixation selection to accomplish corner search. The result is a new algorithm for finding corners which is also a corner-based algorithm for aiming computed foveated visual fixations. In the algorithm, long saccades move the fovea to previously unexplored areas of the image, while short saccades improve the accuracy of putative corner locations. The system is tested on two natural scenes. As an interesting comparison study we compare fixations generated by the algorithm with those of subjects viewing the same images, whose eye movements are being recorded by an eyetracker. The comparison of fixation patterns is made using an information-theoretic measure. Results show that the algorithm is a good locator of corners, but does not correlate particularly well with human visual fixations. The second step is to use the corners located, which meet certain goodness criteria, as keypoints in a modified version of the SIFT algorithm. Two scales are implemented. This implementation creates a database of SIFT features of known objects. To recognize an unknown object, a corner is located and a feature vector created. The feature vector is compared with those in the database of known objects. The process is continued for each corner in the unknown object until enough information has been accumulated to reach a decision. The system was tested on 78 gray scale objects, hand tools and airplanes, and shown to perform well.Electrical and Computer Engineerin

Aligning computer and human visual representations

Both computer vision and human visual system target the same goal: to accomplish visual tasks easily via a set of representations. In this thesis, we study to what extent representations from computer vision models align to human visual representations. To study this research question we used an interdisciplinary approach, integrating methods from psychology, neuroscience and computer vision. Such an approach is aimed to provide new insight in the understanding of human visual representations. In the four chapters of the thesis, we tested computer vision models against brain data obtained with electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI). The main findings can be summarized as follows; 1) computer vision models with one or two computational stages correlate to visual representations of intermediate complexity in the human brain, 2) models with multiple computational stages correlate best to the hierarchy of representations in the human visual system, 3) computer vision models do not align one-to-one to the temporal hierarchy of representations in the visual cortex and 4) not only visual but also semantic representations correlate to representations in the human visual system

Computational Modeling of Human Dorsal Pathway for Motion Processing

Reliable motion estimation in videos is of crucial importance for background iden- tification, object tracking, action recognition, event analysis, self-navigation, etc. Re- constructing the motion field in the 2D image plane is very challenging, due to variations in image quality, scene geometry, lighting condition, and most importantly, camera jit- tering. Traditional optical flow models assume consistent image brightness and smooth motion field, which are violated by unstable illumination and motion discontinuities that are common in real world videos. To recognize observer (or camera) motion robustly in complex, realistic scenarios, we propose a biologically-inspired motion estimation system to overcome issues posed by real world videos. The bottom-up model is inspired from the infrastructure as well as functionalities of human dorsal pathway, and the hierarchical processing stream can be divided into three stages: 1) spatio-temporal processing for local motion, 2) recogni- tion for global motion patterns (camera motion), and 3) preemptive estimation of object motion. To extract effective and meaningful motion features, we apply a series of steer- able, spatio-temporal filters to detect local motion at different speeds and directions, in a way that\u27s selective of motion velocity. The intermediate response maps are cal- ibrated and combined to estimate dense motion fields in local regions, and then, local motions along two orthogonal axes are aggregated for recognizing planar, radial and circular patterns of global motion. We evaluate the model with an extensive, realistic video database that collected by hand with a mobile device (iPad) and the video content varies in scene geometry, lighting condition, view perspective and depth. We achieved high quality result and demonstrated that this bottom-up model is capable of extracting high-level semantic knowledge regarding self motion in realistic scenes. Once the global motion is known, we segment objects from moving backgrounds by compensating for camera motion. For videos captured with non-stationary cam- eras, we consider global motion as a combination of camera motion (background) and object motion (foreground). To estimate foreground motion, we exploit corollary dis- charge mechanism of biological systems and estimate motion preemptively. Since back- ground motions for each pixel are collectively introduced by camera movements, we apply spatial-temporal averaging to estimate the background motion at pixel level, and the initial estimation of foreground motion is derived by comparing global motion and background motion at multiple spatial levels. The real frame signals are compared with those derived by forward predictions, refining estimations for object motion. This mo- tion detection system is applied to detect objects with cluttered, moving backgrounds and is proved to be efficient in locating independently moving, non-rigid regions. The core contribution of this thesis is the invention of a robust motion estimation system for complicated real world videos, with challenges by real sensor noise, complex natural scenes, variations in illumination and depth, and motion discontinuities. The overall system demonstrates biological plausibility and holds great potential for other applications, such as camera motion removal, heading estimation, obstacle avoidance, route planning, and vision-based navigational assistance, etc

On the relationship between neuronal codes and mental models

Das übergeordnete Ziel meiner Arbeit an dieser Dissertation war ein besseres Verständnis des Zusammenhangs von mentalen Modellen und den zugrundeliegenden Prinzipien, die zur Selbstorganisation neuronaler Verschaltung führen. Die Dissertation besteht aus vier individuellen Publikationen, die dieses Ziel aus unterschiedlichen Perspektiven angehen. Während die Selbstorganisation von Sparse-Coding-Repräsentationen in neuronalem Substrat bereits ausgiebig untersucht worden ist, sind viele Forschungsfragen dazu, wie Sparse-Coding für höhere, kognitive Prozesse genutzt werden könnte noch offen. Die ersten zwei Studien, die in Kapitel 2 und Kapitel 3 enthalten sind, behandeln die Frage, inwieweit Repräsentationen, die mit Sparse-Coding entstehen, mentalen Modellen entsprechen. Wir haben folgende Selektivitäten in Sparse-Coding-Repräsentationen identifiziert: mit Stereo-Bildern als Eingangsdaten war die Repräsentation selektiv für die Disparitäten von Bildstrukturen, welche für das Abschätzen der Entfernung der Strukturen zum Beobachter genutzt werden können. Außerdem war die Repräsentation selektiv für die die vorherrschende Orientierung in Texturen, was für das Abschätzen der Neigung von Oberflächen genutzt werden kann. Mit optischem Fluss von Eigenbewegung als Eingangsdaten war die Repräsentation selektiv für die Richtung der Eigenbewegung in den sechs Freiheitsgraden. Wegen des direkten Zusammenhangs der Selektivitäten mit physikalischen Eigenschaften können Repräsentationen, die mit Sparse-Coding entstehen, als frühe sensorische Modelle der Umgebung dienen. Die kognitiven Prozesse hinter räumlichem Wissen ruhen auf mentalen Modellen, welche die Umgebung representieren. Wir haben in der dritten Studie, welche in Kapitel 4 enthalten ist, ein topologisches Modell zur Navigation präsentiert, Es beschreibt einen dualen Populations-Code, bei dem der erste Populations-Code Orte anhand von Orts-Feldern (Place-Fields) kodiert und der zweite Populations-Code Bewegungs-Instruktionen, basierend auf der Verknüpfung von Orts-Feldern, kodiert. Der Fokus lag nicht auf der Implementation in biologischem Substrat oder auf einer exakten Modellierung physiologischer Ergebnisse. Das Modell ist eine biologisch plausible, einfache Methode zur Navigation, welche sich an einen Zwischenschritt emergenter Navigations-Fähigkeiten in einer evolutiven Navigations-Hierarchie annähert. Unser automatisierter Test der Sehleistungen von Mäusen, welcher in Kapitel 5 beschrieben wird, ist ein Beispiel von Verhaltens-Tests im Wahrnehmungs-Handlungs-Zyklus (Perception-Action-Cycle). Das Ziel dieser Studie war die Quantifizierung des optokinetischen Reflexes. Wegen des reichhaltigen Verhaltensrepertoires von Mäusen sind für die Quantifizierung viele umfangreiche Analyseschritte erforderlich. Tiere und Menschen sind verkörperte (embodied) lebende Systeme und daher aus stark miteinander verwobenen Modulen oder Entitäten zusammengesetzt, welche außerdem auch mit der Umgebung verwoben sind. Um lebende Systeme als Ganzes zu studieren ist es notwendig Hypothesen, zum Beispiel zur Natur mentaler Modelle, im Wahrnehmungs-Handlungs-Zyklus zu testen. Zusammengefasst erweitern die Studien dieser Dissertation unser Verständnis des Charakters früher sensorischer Repräsentationen als mentale Modelle, sowie unser Verständnis höherer, mentalen Modellen für die räumliche Navigation. Darüber hinaus enthält es ein Beispiel für das Evaluieren von Hypothesn im Wahr\-neh\-mungs-Handlungs-Zyklus.The superordinate aim of my work towards this thesis was a better understanding of the relationship between mental models and the underlying principles that lead to the self-organization of neuronal circuitry. The thesis consists of four individual publications, which approach this goal from differing perspectives. While the formation of sparse coding representations in neuronal substrate has been investigated extensively, many research questions on how sparse coding may be exploited for higher cognitive processing are still open. The first two studies, included as chapter 2 and chapter 3, asked to what extend representations obtained with sparse coding match mental models. We identified the following selectivities in sparse coding representations: with stereo images as input, the representation was selective for the disparity of image structures, which can be used to infer the distance of structures to the observer. Furthermore, it was selective to the predominant orientation in textures, which can be used to infer the orientation of surfaces. With optic flow from egomotion as input, the representation was selective to the direction of egomotion in 6 degrees of freedom. Due to the direct relation between selectivity and physical properties, these representations, obtained with sparse coding, can serve as early sensory models of the environment. The cognitive processes behind spatial knowledge rest on mental models that represent the environment. We presented a topological model for wayfinding in the third study, included as chapter 4. It describes a dual population code, where the first population code encodes places by means of place fields, and the second population code encodes motion instructions based on links between place fields. We did not focus on an implementation in biological substrate or on an exact fit to physiological findings. The model is a biologically plausible, parsimonious method for wayfinding, which may be close to an intermediate step of emergent skills in an evolutionary navigational hierarchy. Our automated testing for visual performance in mice, included in chapter 5, is an example of behavioral testing in the perception-action cycle. The goal of this study was to quantify the optokinetic reflex. Due to the rich behavioral repertoire of mice, quantification required many elaborate steps of computational analyses. Animals and humans are embodied living systems, and therefore composed of strongly enmeshed modules or entities, which are also enmeshed with the environment. In order to study living systems as a whole, it is necessary to test hypothesis, for example on the nature of mental models, in the perception-action cycle. In summary, the studies included in this thesis extend our view on the character of early sensory representations as mental models, as well as on high-level mental models for spatial navigation. Additionally it contains an example for the evaluation of hypotheses in the perception-action cycle

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans