Concise Computer Vision

Klette, Reinhard

doi:10.1007/978-1-4471-6320-6

Cited by 233 publications

(101 citation statements)

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“…The scale factor for the sliding window is set to the default value, 1.1. Notably, the values of precision and recall (Klette, 2014) remain very similar in both cases but the percentage of rejected windows when only the sign is checked is significantly larger. The time required by the algorithm to process the entire test bench-on an Intel Core i7 at 2.4GHz-is thus reduced by more than 50%.…”

Section: Re-design Of the First Stagementioning

confidence: 81%

High-level Performance Evaluation of Object Detection based on Massively Parallel Focal-plane Acceleration Requiring Minimum Pixel Area Overhead

Parra-Barrero

Fernández-Berni

Oliveira

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Abstract:Smart CMOS image sensors can leverage the inherent data-level parallelism and regular computational flow of early vision by incorporating elementary processors at pixel level. However, it comes at the cost of extra area having a strong impact on the sensor sensitivity, resolution and image quality. In this scenario, the fundamental challenge is to devise new strategies capable of boosting the performance of the targeted vision pipeline while minimally affecting the sensing function itself. Such strategies must also feature enough flexibility to accommodate particular application requirements. From these high-level specifications, we propose a focal-plane processing architecture tailored to speed up object detection via the Viola-Jones algorithm. This architecture is supported by only two extra transistors per pixel and simple peripheral digital circuitry that jointly make up a massively parallel reconfigurable processing lattice. A performance evaluation of the proposed scheme in terms of accuracy and acceleration for face detection is reported.

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Section: Re-design Of the First Stagementioning

confidence: 81%

High-level Performance Evaluation of Object Detection based on Massively Parallel Focal-plane Acceleration Requiring Minimum Pixel Area Overhead

Parra-Barrero

Fernández-Berni

Oliveira

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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“…GLCM is computed by calculating how often a pair of pixels with the same intensity values occur in an image. In addition, the moving distance and angle between the target pixel and the others need to be defined [39]. A set of statistical features were proposed by Haralick [40] so as to define the spatial relationships between the neighbouring pixels (textural properties).…”

Section: Extraction Of Spatial Featuresmentioning

confidence: 99%

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

et al. 2018

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This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).

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“…Basically CV aims to acquire, process, analyse describe, and understand at best the content of an imagery data [22][23][24][25]. Some of the typical tasks of CV are, for instance, object recognition (also called object classification), pose estimation, image segmentation, object tracking in image sequences.…”

Section: Computer Vision -2d and 3d Data Focused Approachmentioning

confidence: 99%