Analisis Dan Implementasi Edge Detection Pada Citra Digital Menggunakan Gabor Filters

ABSTRAKSI: Salah satu proses pra pengolahan citra ialah edge detection. Proses ini bertujuan untuk memperjelas garis-garis batas yang terdapat pada citra. Edge detection memiliki sifat memperkuat komponen berfrekuensi tinggi. Dengan adanya hal ini, bentuk dasar dari sebuah obyek akan terlihat jelas. Dalam proses edge detection, terdapat beberapa teknik yang dapat digunakan. Dalam tugas akhir ini menerapkan metode Gabor Filters dalam mencari edge (tepi) pada suatu citra masukan. Analisis dari hasil proses ini dilakukan melalui penilaian kuantatif dan kualitatif. Penilaian secara kuantatif, yaitu dengan menghitung nilai error rate, false edge, dan missing edge yang dihubungkan dengan penambahan noise terlebih dahulu terhadap citra masukan. Sedangkan penilaian secara kualitatif, yaitu dengan pengamatan mata manusia. Dari hasil percobaan yang dilakukan, metode Gabor Filters merupakan salah satu cara yang tepat dalam megenali bentuk objek. Akan tetapi kelemahan yang terdapat dalam Gabor Filters adalah metode ini sangat rentan terhadap noise dan perubahan nilai pixel pada citra, hal ini ditunjukkan berdasarkan hasil penilaian kualitatif dan tingkat Error Rate yang cukup tinggi.Kata Kunci : edge detection, Gabor Filters, error rate, false edge, missingABSTRACT: shows an image��s edges. The advantage of edge detection is for showing high frequency components. In other words, basic sketch of an object will be seen clearly with edge detection process. It has some methods which can be used.Edge detection method in this final assignment is Gabor Filters to find edges on sample image. This process analysis had done by using quantitative and qualitative test. Quantitative test is account error rate, false edge and missing edge which is connected with generating noise to sampling image at first. Therefore, qualitative test is using human visual.Gabor Filters is one of the right method for recognize object based on experiment. In spite of that the weakness in Gabor Filters are this method is very sensitive with noise and pixel point transition on image,based on qualitative testing and Error Rate value.Keyword: edge detection, Gabor Filters, error rate, false edge, missin

Improvements on Gabor Descriptor Retrieval for Patch Detection

The localization of object parts in the component-based object detection is among the main tasks to solve. This paper presents several improvements of the proposed local image descriptor based on Gabor wavelets. Including these descriptors in the desired application is an ambitious challenge if we take into account the high number of parameters. Determining of parameters can be very hard because of their infinite definition range. Defining the filters is done in two stages: a theoretical consideration narrows the domain and the cardinality of parameters; this is followed by adequate experiments to select the most characteristic descriptor for a target image patch. The descriptor is created from a given number of 2D Gabor filters chosen by the GentleBoost learning algorithm. Comparing the proposed descriptor to those found in the state of the art, we can conclude that the selected filters are adaptable to any target object. In contrast to this, the majority of filter-based descriptors have fixed values for the parameters that do not allow to be ductile to the given object. Parameters fine-tuning allows the descriptor to be general, and discriminative at the same time. The effect of the following experiments has been analyzed during the investigation: elimination of redundancy between the weak classifiers, using the LoG interest points in the detection process. Finally, we propose an acceleration algorithm in order to deter- mine the response map faster. By means of the descriptor, the response map is created, which accurately localizes the target object part and can easily be integrated in almost all detection systems

Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc

A preliminary approach to intelligent x-ray imaging for baggage inspection at airports

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted

Gabor Filter and Rough Clustering Based Edge Detection

This paper introduces an efficient edge detection method based on Gabor filter and rough clustering. The input image is smoothed by Gabor function, and the concept of rough clustering is used to focus on edge detection with soft computational approach. Hysteresis thresholding is used to get the actual output, i.e. edges of the input image. To show the effectiveness, the proposed technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Ventral-stream-like shape representation : from pixel intensity values to trainable object-selective COSFIRE models

Keywords: hierarchical representation, object recognition, shape, ventral stream, vision and scene understanding, robotics, handwriting analysisThe remarkable abilities of the primate visual system have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 → V4 → TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition. An S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work. We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot. S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms.peer-reviewe

2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for both dimension reduction and good recognition performance when compared to the complete Gabor filter bank. The system has been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and achieved average recognition rate of 98.9 %