CMOS imagers are complex systems whose design requires quite different pieces of expertise, namely: pixels, analog signal processing, pixel readout and analog-to-digital conversion, digital signal processing, output d...
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CMOS imagers are complex systems whose design requires quite different pieces of expertise, namely: pixels, analog signal processing, pixel readout and analog-to-digital conversion, digital signal processing, output drivers, etc. Confronting the design of new imagers require hence the concourse of multidisciplinary teams. However, because correct operation calls for the close interconnection among the different parts, global knowledge is mandatory for successful design. This is particularly pertinent for the newer generations of smart imagers required for high-end applications and/or requiring ultra-high image capture, on-chip image correction, scene interpretation, high dynamic range capture, etc. All these features demand architectural and circuital innovations and pose significant challenges to designers. Also, the increased interest on sensors capable of capturing 3-D scenes raise new challenges at circuit level related to the necessity to interface pixels different from those employed for 2-D capture, on the one hand, and to extract and convert to digital domain time information, on the other hand. This tutorial addresses the design of smart CMOS imagers by following a comprehensive and complete top-down approach where each subsystem is contemplated and described as a part of a whole. Starting the formulation of the performance metrics used to specify and characterize imagers, the tutorial explains how the subsystem behavior and non-idealities impact on the global imager metrics, thereby setting the basis to specify the subsystems for given global image sensor specs. Such methodology is illustrated in the tutorial via a dedicated, MATLAB-based modeling tool which will be employed to allow the attendees gaining insight on the impact of non-ideal sub-systems behaviors. The tutorial overviews the state-of-the-art regarding: pixels; analog signal processing and read-out circuitry; data conversion circuitry, covering both amplitude data converters (required for 2-D
We present a novel shape from focus method for high- speed shape reconstruction in optical microscopy. While the traditional shape from focus approach heavily depends on presence of surface texture, and requires a con...
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We present a novel shape from focus method for high- speed shape reconstruction in optical microscopy. While the traditional shape from focus approach heavily depends on presence of surface texture, and requires a considerable amount of measurement time, our method is able to perform reconstruction from only two images. Our method relies the rapid projection of a binary pattern sequence, while object is continuously moved through the camera focus range and a single image is continuously exposed. Deconvolution of the integral image allows a direct decoding of binary pattern and its associated depth. Experiments a synthetic dataset and on real scenes show that a depth map can be reconstructed at only 3% of memory costs and fraction of the computational effort compared with traditional shape from focus.
On the base of the method of Support vector data description (SVDD), this paper proposes a SVDD method based on maximum distance between two centers of spheres. It applies two hyperspheres to separate two kinds of tra...
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On the base of the method of Support vector data description (SVDD), this paper proposes a SVDD method based on maximum distance between two centers of spheres. It applies two hyperspheres to separate two kinds of training instance and maximize distance between two centers of hyperspheres at the same time. Experimental results show that the method is effective, especially for unbalance problem, our method can get better results than all other methods.
Biopsies involve taking samples from living tissue using a biopsy needle. In current clinical practice they are a first mandatory step before any further medical actions are planned. Performing a biopsy on a deep seat...
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This paper shows that the principles of video coding that are related to removing temporal redundancy by means of motion estimation and compensation can be successfully used to compress still images. If all polyphase ...
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This paper shows that the principles of video coding that are related to removing temporal redundancy by means of motion estimation and compensation can be successfully used to compress still images. If all polyphase components of an image are identified with correlated video frames, only one of them needs to be intra-coded, as the rest can be encoded using mainly bidirectional prediction. Using the H.264 reference software, it has been experimentally verified that the approach offers compression comparable to intra-coding the whole image as a single video frame, the common way of applying the H.264 standard to still pictures. As the H.264 encoder is not optimized for processing polyphase components, the results suggest that, based on the presented idea, it is possible to develop a new image codec that could compete with the state-of-art algorithms.
In this paper we propose a general variable neighborhood search approach for the balanced location problem. Next to large shaking neighborhoods, the embedded variable neighborhood descent utilizes three neighborhood s...
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Volumetric data is common in medicine, geology and engineering, but the O(n 3) complexity in data and algorithms has prevented the widespread use of volume graphics. Recently, 3D image processing and visualization alg...
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One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are eit...
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One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are either based on direct modeling of the human appearance or on machine learning. In this work, we aim at taking advantage of both directions of research. On the one hand side, we compute a descriptive appearance representation encoding the vertical color structure of pedestrians. To improve the classification results, we additionally estimate the transition between two cameras using a pair-wisely estimated metric. In particular, we introduce 4D spatial color histograms and adopt Large Margin Nearest Neighbor (LMNN) metric learning. The approach is demonstrated for two publicly available datasets, showing competitive results, however, on lower computational costs.
The research on facial animation has grown very fast and become more realistic in term of 3D facial data since the laser scan and advance 3D tools can support creating complex facial model. However, that approaches st...
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Two-dimensional transfer functions are an effective and well-accepted tool in volume classification. The design of them mostly depends on the user's experience and thus remains a challenge. Therefore, we present a...
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