Bhupinder Verma ¹, Gurpreet Kaur ²

Affiliations
1 PTU Kapurthala, Jalandhar - 144603, Punjab, IN
2 CT Group of Institutions, Jalandhar - 144008, Punjab, IN

Abstract

Objectives: This paper presents a Morphological operations based software code in MATLAB for counting connected rice kernels from digital image and to grade the quality of samples by a methodology used by the Bureau of Indian standards. Methods/Statistical Analysis: The proposed code may further be transferred on to the reprogrammable hardware devices like FPGA by converting the entire code into VHDL (VHSIC Hardware Description Language) with the help of Simulink HDL Coder; making it a hardware compatible code. The efficiency of proposed code is tested over the digital images of rice grain samples with complex backgrounds or captured under poor illumination conditions. Findings: 100% accuracy has been observed in the counting efficiency of software and hardware codes by successfully separating touching kernels. The automatically generated VHDL code through HDL Coder is also successfully synthesized over the FPGA in Xilinx ISE and is compared for its accuracy and processing time with simulated results. It is concluded that a real time image processing algorithm, when design effectively to get synthesized over the FPGAs, may yield faster results with processing time of few seconds, whereas the manual methods or simulated methods are much slower in terms of their speed. Application/ Improvements: The proposed approach may further be utilized to design a portable FPGA based hardware prototype to grade the quality of rice samples by completing eliminating the manual investigation done by humans as well as by computer based simulated inspection.

Keywords

Embedded Imaging, FPGA, HDL Coder, Image Processing, MATLAB, VHDL, Xilinx ISE.

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Shifali ¹, Gurpreet Kaur ¹

Affiliations
1 Department of Computer Science and Engineering, Chandigarh University, Gharuan - 140413,Punjab, IN

Abstract

Objectives: The objective of proposed method is to improve the accuracy and performance of an image. To apply the segmentation on satellite image using a median filter and the Scale Invariant Feature Transformation algorithm for feature extraction from an image. And to implement the Back Propagation Neural Network algorithm so as to reduce the mean square error rate, false acceptance and rejection rate and to improve the accuracy of an image. Methods/Statistical Analysis: The single step pre-processing of an image is done to make it suitable for segmentation. For segmentation, median filter is used and the Scale Invariant Feature Transformation (SIFT) algorithm is used for feature extraction. When features from the image are generated then the image is optimized using a Genetic Algorithm. After image optimization Back Propagation Neural Network is used to classify the image based on different parameters. Our proposed technique is a Genetic Algorithm for an image optimization and Back Propagation Neural Network (BPNN) for classification of the satellite image. Findings: The image feature extraction using Scale Invariant Feature Transformation (SIFT) method results in better feature extraction as it gives both the key distribution points saved in database and image. The mean square error using GA-BPNN is less than existing technique ABC-FCM which gives better performance. GA-BPNN technique gives more accuracy which is approximately 99.91 as compared to other methods. Application/Improvements: The proposed technique has been tested with the images of different resolution and the results obtained by BPNN are proven to be better than the ABC-FCM. The proposed method can be used for different types of images and also for medical images.

Keywords

Back Propagation Neural Network, Genetic Algorithm, Image Classification, Optimization, Segmentation.

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Gurpreet Kaur ¹, Prateek Agrawal ¹

Affiliations
1 School of Computer Science Engineering, Lovely Professional University, Phagwara - 144411, Punjab, IN

Abstract

Background/Objectives: Image fusion is the technique which merges the input images to obtain the focused single image. A new method is proposed in this paper to optimise image fusion using feature matching based Scale Invariant Feature Transform (SIFT) and Random Sample Consensus (RANSAC). Methods/Analysis: In our proposed method, two input images are fused using Stationary Wavelet Transform to get a single focused image. Then, feature matching technique called SIFT is applied to match the corresponding features between two images. Further, RANSAC is applied to further optimise the result of SIFT and get a final fused image. Findings: Quantitative and visual results show that a highly focused and better fused image is obtained after feature matching with SIFT and further refinement with RANSAC. The proposed method is robust and independent of scale, light intensity, orientation of camera etc. Applications: The methodology for image fusion may be applied to stereo-images. Feature matching based on SIFT and RANSAC may be used to reconstruct a 3D view from stereo-images.

Keywords

Image Fusion, RANSAC, SIFT, Stationary Wavelet Transform.

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