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The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network (CNN) using a minimal model (Minimal CNN). The proposed minimal CNN is presented using a layering approach. This approach provides a concise and accessible understanding of the main mathematical operations of a CNN. Hence, it benefits...
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend...
Due to variations in pose, angle and illumination condition, a person's appearance is significantly different in two different views, which makes person re-identification(re-id) intrinsically difficult. In this paper, we propose a person re-id method which learns Convolutional Neural Networks (CNNs) feature representations from joint-dataset learning. The CNN features extracted from all levels of...
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive...
Chinese traditional visual culture symbols (CT-VCSs) is formed in the tradition and has the characteristic of Chinese unique ideological and cultural connotation. It is a visual cultural heritage of Chinese culture. So the research on CT-VCSs has important practical significance. In this paper, it is mainly about the recognition and classification of CT-VCSs based on machine learning. We make use...
The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This "oblique effect" has been researched and confirmed in numerous research studies, including behavioral studies and neurophysiological and neuroimaging findings. Although the "oblique effect" has influence in many fields, little research integrated it into computational...
Deep Convolutional Neural Network (CNN) is one of the most popular methods for image processing and recognition. There are many research works to improve the performance of CNNs. However, as an important part of CNNs, convolution kernel has rarely been discussed. As one Original Convolution Kernel (OCK) can only detect one type of visual feature with a fixed deformation, the networks using OCKs may...
Finding the location of a mobile user is a classical and important problem in pervasive computing, because location provides a lot of information about the situation of a person from which adaptive computer systems can be created. While the inference of location outside buildings is possible with GPS or similar satellite systems, these are unavailable inside buildings. A large number of methods has...
Recently, convolutional neural network (CNN) models have achieved great success in many vision tasks. However, few attempts have been made to explore CNN for online model-free object tracking without time-consuming offline training. In this paper, we propose an online convolutional network (OC-N) for visual object tracking. To make the network less dependent on labeled data, K-means is employed to...
We propose the technique of the semi-automatic image creation. By this we mean an automatic completion of an image that is partially defined on the given domain. The essential feature of this technique is that the complementary area is much larger than that where the image is defined. Moreover, the proposed technique can be used in image upsampling, image inpainting, etc. In this contribution, we...
No matter the end-effector control of joint robots or the pose control of mobile robots, the image feature detection is the critical component of these robot technologies. Moreover, the interest point is the most widely used feedback signal in visual servo control, which performances are directly effected by the actual execution time of the feature detection methods. However, in the robot visual servo...
High performance and energy efficient video analytics systems that can extract rich metadata from voluminous visual content, will enable a variety of high-value surveillance, driver assistance, video tagging, and first person analytics systems. These big-data applications are pervasive across retail, automotive, medical, agriculture and security domains. However, current trends in general purpose...
In order to emphasize features in flow textures while preserving the orientation of flows in 2D fields, a new method to generate flow textures with non-uniform streamlets is proposed in this paper. In the method, a control grid is built and divided into different regions that reflect the complexity of the underlying flow field. The resulting regions are then used to control to the distribution of...
This paper addresses the problem of image alignment using direct intensity-based methods for affine and homography transformations. Direct methods often employ scale-space smoothing (Gaussian blur) of the images to avoid local minima. Although, it is known that the isotropic blur used is not optimal for some motion models, the correct blur kernels have not been rigorously derived for motion models...
Navigation and way finding including obstacle avoidance is difficult when visual perception is limited to low resolution, such as is currently available on a bionic eye. Depth visualisation may be a suitable alternative. Such an approach can be evaluated using simulated phosphenes with a wearable mobile virtual reality kit. In this paper, we present two novel approaches: (i) an implementation of depth...
Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. Address-Event-Representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI spike-based chips. These neuro-inspired implementations allow developing complex, multilayer, multichip neuromorphic systems and have been used...
Human brain is composed by millions of parallel neurons that process the visual information in a continuous way, spike by spike, from the information received through the retina. In this processing there are no frames in the video like it occurs in digital video. One frame every 40 ms implies information loss in between two frames. Address-Event-Representation (AER) is mechanism for multiplexing in...
In this paper a novel implementation of the saliency map model on a multi-GPU platform using CUDA technology is presented. The saliency map model is a well-known computational model for bottom-up attention selection and serves as a basis of many attention control strategies of cognitive vision systems. A real-time implementation is the prerequisite of an application of bottom-up attention on mobile...
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