High Speed and Area Efficient 2D DWT Processor Based Image Compression

Kaur, Sugreev; Mehra, Rajesh

doi:10.5121/sipij.2010.1203

Cited by 18 publications

(15 citation statements)

References 10 publications

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“…Experimental results show that this method always produces good output, even when tested with the high level of noise. Both the simulation and computational complexity analysis show that the proposed method is better than existing standard median filter (MED), adaptive median filter (AMED), Kaliraj et al filter [15] and Predictive-based adaptive switching median filter (PASMF). Some points can be discussed for further research.…”

Section: Resultsmentioning

confidence: 94%

“…Here Size of gray scale image is 512 × 512. This Image quality index models is a combination of three different factors: loss of correlation, luminance distortion, and contrast distortion[6].IQIw = Corr(Ow, Rw) × Lum(Ow, Rw) × Cont(Ow, Rw)(The local quality index IQIw is computed within a particular sliding window W Ow and Rw represent the sliding window of original and restored images, respectively[8,15].The dynamic range of IQI is [-1, 1]. The best value 1 is achieved if and only if restored image R is equal to the original image O.…”

mentioning

confidence: 99%

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Median Filter for Noise Removal using Particle Swarm Optimization

Mehra¹,

Verma²

2016

IJCA

Self Cite

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Adaptive median filter has been an efficient algorithm for salt and pepper noise removal. But, if the noise percentage are very high, adaptive median filter may still remain noise regions in result image. So a Particle swarm optimization based novel and modified adaptive median filter (PSOMF) is proposed. The Proposed filter works in two stages: Noise detection stage and noise filtering stage. Particle swarm optimization is a simple algorithm that seems to be effective for optimizing a wide range of functions. Noise Detection stage works on it. First, a test decides whether or not a given pixel is contaminated by impulse noise. If contaminated, a median fitter is applied. Simulation results show that our method is significantly better than a number of existing techniques in term of image restoration and noise detection, even for noise levels as high as 90%.

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Section: Resultsmentioning

confidence: 94%

mentioning

confidence: 99%

Median Filter for Noise Removal using Particle Swarm Optimization

Mehra¹,

Verma²

2016

IJCA

Self Cite

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“…The image therefore contains more noise in those regions. As the details' values represent the high frequency parts in the image (Farge, 1992;Kaur and Mehra, 2010), they show higher values with increasing distance from the camera. This effect was even higher in topview sequences than in sideview sequences.…”

Section: Discussionmentioning

confidence: 99%

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Salau

Haas

Thaller

et al. 2016

animal

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Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore-or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.Keywords: dairy cattle, monitoring system, 3D camera, object recognition, wavelet transform ImplicationThe prototype of a multi-Kinect system is presented that captures 3D data of dairy cows in motion for health monitoring. When fully developed, it will provide an inexpensive means of automated early lameness detection and livestock monitoring, thereby helping to increase animal welfare. The large amount of recorded data will provide a solid basis for scientific analyses and can help to deepen the understanding of connections between traits and genetics. Object recognition and methods to analyze the animals' surfaces from the 3D data are important developmental steps that are approached using the wavelet transform. IntroductionSeveral camera-based studies were conducted over the last years yielding high rates of correct classification in lameness detection in dairy cows. Song et al. (2008), Pluk et al. (2012) and Viazzi et al. (2013) used digital 2D cameras to record walking cows from the side at distances of 3 to 6 m and analyzed step overlap, fetlock joints' angles or back posture. There were also several camera-based studies dealing with body condition score (BCS) determination. Azzaro et al. (2011) used linear and polynomial kernel principal analysis to reconstruct cows' shapes. Bercovich et al. (2012) presented BCS-prediction models that were based on five anatomical points. In both studies the animals were observed from a top view position using 2D cameras. The definitio...

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“…A discretised version of this transform (Discrete Wavelet Transform) is generally used for filtering, denoising and compression [13].…”

Section: Development Of Wavelet Analysismentioning

confidence: 99%