Introduction to Video

Jack, Keith

doi:10.1016/b978-075068395-1/50002-x

Cited by 11 publications

(15 citation statements)

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“…After clamping, T 1 is tuned off, and V in is the analog video information superposed on the DC clamping voltage, V CLP . Typically, "clamp to ground" and "clamp to mid-scale" are the two most common strategies for clamping [4]. The latter requires a relatively higher clamping voltage than the former.…”

Section: Circuit Analysis and Designmentioning

confidence: 98%

A switched-capacitor programmable-gain amplifier for high-definition video analog front-ends

Zhang

Xue

Chen

et al. 2012

2012 IEEE 11th International Conference on Solid-State and Integrated Circuit Technology

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A switched-capacitor programmable gain amplifier (PGA) for high-definition video analog front-ends (AFEs) is presented. The PGA also acts as the sample and holder of the ADC to reduce the power consumption and chip area of the whole AFE. The 9-bit gain and offset control DACs are embedded into the switched capacitor networks of the PGA. Simulated results show that the PGA achieves a gain range of 0.9 to 2.4V/V, and an offset control range of -230 to 230mV while consuming 10.1mA from a 1.8V power supply. An AFE based on the proposed PGA is fabricated in TSMC 0.18m process. Measurement results show that the AFE achieves the capability to process variant HDTV signals up to 1080p YPbPr and computer video signals up to UXGA (1600x1200 @75Hz).

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Section: Circuit Analysis and Designmentioning

confidence: 98%

A switched-capacitor programmable-gain amplifier for high-definition video analog front-ends

Zhang

Xue

Chen

et al. 2012

2012 IEEE 11th International Conference on Solid-State and Integrated Circuit Technology

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“…For dynamic color textures, we transform the colored frames into YUV color space 23 where ........................................................................................ we have totally 3 × τ images, and do multi-resolution analysis on each image. We observe that the structural information is more important than the color signals.…”

Section: Dynamic-textures Synthesismentioning

confidence: 99%

Multi‐resolution parametric synthesis of manipulative dynamic textures

Sun²,

Wang³

2008

Computer Animation & Virtual

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Dynamic textures are composed of spatially multi-scale and temporally coherent visual signals. In this paper, we propose a novel approach for multi-resolution parametric synthesis (MPS) of manipulative dynamic textures, including learning, analysis, and motion manipulation of layered feature spaces. The proposed approach separates the motions into different scales or frequencies, and the dynamic feature spaces are analyzed in modeling and then mapped to a global coordinate system to expose the multi-scale structures. Our approach emphasizes the importance of fine-scale data appearing more informative, while coarse signals are less focused. The synthesized dynamic textures using our MPS approach are strongly enhanced in dynamic appearance than the single-scale LDS model, and also flexible in manipulating the decomposed feature spaces for creating different dynamic textures of the real world. Our experimental results showed the more faithful synthesized dynamic textures from input short videos, and their manipulative controls by simple editing of feature space basis, not only the whole video data. Copyright

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“…However, since many methods for converting to grayscale have been employed in computer vision, we believe it is prudent to assess whether this assumption is warranted. The most common techniques are based on weighted means of the red, green, and blue image channels (e.g., Intensity and Luminance ), but some methods adopt alternative strategies to generate a more perceptually accurate representation (e.g., Luma and Lightness [1]) or to preserve subjectively appealing color contrast information in grayscale images (e.g., Decolorize [2]). A priori, none of these criteria suggest superior recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Color-to-Grayscale: Does the Method Matter in Image Recognition?

2012

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In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures.

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Introduction to Video

Cited by 11 publications

References 0 publications

A switched-capacitor programmable-gain amplifier for high-definition video analog front-ends

A switched-capacitor programmable-gain amplifier for high-definition video analog front-ends

Multi‐resolution parametric synthesis of manipulative dynamic textures

Color-to-Grayscale: Does the Method Matter in Image Recognition?

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