Agya Mishra

Illumination Reduction for Low Contrast Color Image Enhancement with Homomorphic Filtering Technique

Image clarity is very easily affected by lighting, weather, or equipment that has been used to capture the image. These conditions lead to image may suffer from loss of information. Quality of the image is highly affected by improper lighting condition or illumination. Therefore removal of such illumination and increase of Image quality is necessary for human perception. In this paper, we propose a novel approach for Illumination correction of such Images. The proposed method uses Homomorphic filtering technique to reduce illumination and compare the results with contrast limited adaptive histogram equalization (CLAHE) technique. Experimental results on test images corrupted by illumination pattern shows that results obtained are visually effective compared with the existing CLAHE method.

Fingerprint Segmentation Using Scale Vector Algorithm

Pre-processing part in fingerprint image recognition system is fingerprint image segmentation. Even after the years of research in the field of image segmentation, it is still a challenging task. An image segmentation algorithm is proposed, named as Adaptive (scale) and Orientation (vector) algorithm, for effective segmentation of fingerprint images. The basic idea of the proposed algorithm is originated from the total variation models, along with two features of fingerprints, namely, scale and vector. It decomposes a fingerprint image into two layers: noisy and texture. Proposed segmentation algorithm is experimented and analyzed for two different fingerprint images. PSNR is the measuring parameter for checking the efficiency of algorithm. This algorithm is compared with adaptive and directional algorithm individually and concludes its better efficiency.

Fingerprint Segmentation Algorithms: A Literature Review

Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. This paper review the different algorithms particularly used for fingerprint segmentation. Adaptive Total Variation Model, Directional Total Variation Model, method based on combination of ridge orientation and frequency features, method based on orientation field information combined with statistical characteristics of gray, Ridge Template Correlation, method using three pixel features(being the coherence, the mean and the variance) are discussed and compared based on their performance and concluded that Adaptive Total Variation Model and Directional Total Variation Model provides better results, so it will be good for the field of researches. General Terms Image recognition, fingerprint image preprocessing, algorithms of fingerprint segmentation.

Speech Enhancement Using Feedback Particle Filter

The paper presents a new speech enhancement algorithm using feedback particle filter. The innovation error based feedback structure is the decisive factor for Feedback particle filter. The robustness is key feature, this variant is found to improve the performance of the system reducing MSE as compared to conventional particle filter at even lower no of particles, without much preprocessing or modeling, gain and innovation error depends on values of variance and means of particle hence dynamically generated. The feedback particle filter outperforms a conventional particle filter.

Fingerprint Segmentation Based on Adaptive and Orientation Algorithm

2 ABSTRACT: Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. Regardless of years of research in the area of general purpose image segmentation, it is still a very challenging task. A new image decomposition scheme, called the Adaptive and Orientation algorithm, is proposed to achieve effective segmentation for fingerprint images in this work. The proposed model is inspired by total variation models, but it differentiates itself by integrating two unique features of fingerprints; namely, scale and orientation. The proposed Adaptive and Orientation algorithm decomposes a fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e.g. structured noise) while the texture layer mainly consists of the latent fingerprint. Proposed segmentation algorithm is experimented and analyzed for two different fingerprint images. The PSNR for image segmentation has been used as a comparison parameter for proposed image segmentation methods.

MIMO-MMSE Model using Multiple Active Antennas for Rayleigh Fading Channel

Spatial Modulation (SM) is a technique that can enhance the capacity of MIMO schemes by exploiting the index of transmit antenna to convey information bits. This paper describes a MIMO transmission scheme that combines SM and spatial multiplexing. In the basic form of SM, only one out of NT available antennas is selected for transmission in any given symbol interval. This paper proposes to use more than one active antenna to transmit several symbols simultaneously. This would increase the spectral efficiency and decreases BER (bit error rate) at the receiver, an MMSE detector is employed to jointly estimate the Transmitted symbols as well as the index of the active transmit antennas. The results show that the detection based on zero forcing is good for noise free channel and is successful in removing ISI, but MMSE is a better choice than ZF in terms of BER characteristics and under Noise performance. In MIMO transmission scheme the uncorrelated fading channel also called statistical model is used for the effect of a propagation environment on a radio signal. Sometimes use of multiple antenna techniques is problem depended but it used because it gives better performance like heavily built urban environment where radio signals are fade and scattered.