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Insects use an estimate of the angular speed of the visual image across the eye (termed optic flow) for a wide variety of behaviors including flight speed control, visual navigation, depth estimation, grazing landings, and visual odometry. Despite the behavioral importance of visual speed estimation, the neuronal mechanisms by which the brain extracts optic flow information from the retinal image remain unknown. This dissertation investigates the underlying neuronal mechanisms of visual speed estimation via three complementary strategies: the development of neuronally-based computational models, testing of the models in a behavioral simulation framework, and behavioral experiments using bumblebees. Using these methods I demonstrate the sufficiency of two non-directional models of motion detection for reproducing real-world, speed dependent behaviors, propose potential neuronal circuits by which these models may be physiologically implemented, and predict the expected responses of these neurons to a range of visual stimuli.

By applying the image processing algorithms used in surveillance systems on video data obtained from a web camera, a motion detection application can be created and incorporated into web based games. The use of motion detection opens up a vast field of new possibilities in game design and this thesis will therefore cover how to create a motion detection JavaScript module which can be used in web based games. The performance and quality of the motion detection algorithms are important to consider when creating an application. What motion detection algorithms can be used to give a qualitativerepresentation without affecting the performance of a web based game will be analyzed and implemented in this thesis. Since the performance of the Central Processing Unit will not suffice, WebGL and the parallelism of the Graphical Processing Unit will be utilized to implement some of the most recognized image processing algorithms used in motion detection systems. The work resulted in an application where Gaussian blur and Frame Subtraction were used to detect and return areas where motion has been detected.

Optical flow and its extensions have been widely used in motion detection and computer vision. In the study, principal component analysis (PCA) is applied to analyze optical flows for better motion detection performance. The joint optical flow and PCA approach can efficiently detect moving objects and suppress small turbulence. It is effective in both static and dynamic background. It is particularly useful for motion detection from outdoor videos with low quality and small moving objects. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms. Saving strategies are developed to reduce computational complexity of optical flow calculation and PCA. Graphic processing unit (GPU)-based parallel implementation is developed, which shows excellent speed up performance.

Yes / When we track an object moving in depth, our eyes rotate in opposite directions. This type of “disjunctive” eye movement is called horizontal vergence. The sensory control signals for vergence arise from multiple visual cues, two of which, changing binocular disparity (CD) and inter-ocular velocity differences (IOVD), are specifically binocular. While it is well known that the CD cue triggers horizontal vergence eye movements, the role of the IOVD cue has only recently been explored. To better understand the relative contribution of CD and IOVD cues in driving horizontal vergence, we recorded vergence eye movements from ten observers in response to four types of stimuli that isolated or combined the two cues to motion-in-depth, using stimulus conditions and CD/IOVD stimuli typical of behavioural motion-in-depth experiments. An analysis of the slopes of the vergence traces and the consistency of the directions of vergence and stimulus movements showed that under our conditions IOVD cues provided very little input to vergence mechanisms. The eye movements that did occur coinciding with the presentation of IOVD stimuli were likely not a response to stimulus motion, but a phoria initiated by the absence of a disparity signal. / Supported by NIH EY018875 (AMN), BBSRC grants BB/M001660/1 (JH), BB/M002543/1 (AW), and BB/MM001210/1 (MB).

INTRODUCTION:Whirligig beetles (Coleoptera: Gyrinidae) are aquatic insects living on the water surface. They are equipped with four compound eyes, an upper pair viewing above the water surface and a lower submerged pair viewing beneath the water surface, but little is known about how their visual brain centers (optic lobes) are organized to serve such unusual eyes. We show here, for the first time, the peculiar optic lobe organization of the larval and adult whirligig beetle Dineutus sublineatus.RESULTS:The divided compound eyes of adult whirligig beetles supply optic lobes that are split into two halves, an upper half and lower half, comprising an upper and lower lamina, an upper and lower medulla and a bilobed partially split lobula. However, the lobula plate, a neuropil that in flies is known to be involved in mediating stabilized flight, exists only in conjunction with the lower lobe of the lobula. We show that, as in another group of predatory beetle larvae, in the whirligig beetle the aquatic larva precociously develops a lobula plate equipped with wide-field neurons. It is supplied by three larval laminas serving the three dorsal larval stemmata, which are adjacent to the developing upper compound eye.CONCLUSIONS:In adult whirligig beetles, dual optic neuropils serve the upper aerial eyes and the lower subaquatic eyes. The exception is the lobula plate. A lobula plate develops precociously in the larva where it is supplied by inputs from three larval stemmata that have a frontal-upper field of view, in which contrasting objects such as prey items trigger a body lunge and mandibular grasp. This precocious lobula plate is lost during pupal metamorphosis, whereas another lobula plate develops normally during metamorphosis and in the adult is associated with the lower eye. The different roles of the upper and lower lobula plates in supporting, respectively, larval predation and adult optokinetic balance are discussed. Precocious development of the upper lobula plate represents convergent evolution of an ambush hunting lifestyle, as exemplified by the terrestrial larvae of tiger beetles (Cicindelinae), in which activation of neurons in their precocious lobula plates, each serving two large larval stemmata, releases reflex body extension and mandibular grasp.

The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.<p>This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.<p>This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.<p>The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.<p>This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.<p>This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.<p>The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.