Segmentasi Berbasis Region Pada Citra Berwarna Untuk Keperluan Temu Kembali Citra Pada Event Olah Raga Lapangan Hijau

Dalam sistem temu kembali citra, segmentasi adalah bagian terpenting dalam tahap awal pemrosesan citra. Metode segmentasi yang tepat sangat mempengaruhi hasil segmentasi suatu citra, terutama pada citra berwarna dan bertekstur yang menjadi permasalahan tersendiri dalam proses segmentasi. Salah satu metode yang diterapkan pada penelitian ini adalah segmentasi berbasis region dengan algoritma JSEG. Proses segmentasi citra dengan algoritma JSEG terbagi atas dua tahap, yaitu proses kuantisasi warna dan proses segmentasi spasial. Penggunaan ruang warna CIE LUV pada tahap kuantisasi warna yang mampu menerima warna menurut persepsi manusia. Warna-warna citra yang terkuantisasi membentuk color class-map untuk membedakan region-region dalam citra. Di tahap segmentasi spasial, dilakukan perhitungan ukuran segmentasi yang “baik� menurut color class-map yang terbentuk pada window lokalnya sehingga menghasilkan “J-image�. Metode region growing digunakan untuk mensegmentasi citra berdasarkan J-image multiskala. Uji coba pada 12 citra event olah raga lapangan hijau dilakukan untuk melihat hasil segmentasi yang sesuai menurut kombinasi nilai parameter threshold yang tepat. Dari variasi parameter threshold kuantisasi warna diperoleh 67% cenderung pada nilai threshold 255 menghasilkan segmentasi yang baik. Sedangkan untuk threshold region merging, cenderung pada nilai threshold 0.4. Hasil eksperimen menunjukkan bahwa dengan kombinasi parameter nilai threshold yang baik ini, memudahkan dalam proses implementasi sistem temu kembali citra menurut hasil segmentasi warn

Shadow Detection using DWT with Multi-Wavelet Selection & user Configurable Variance Parameters

Moving cast shadows are a noteworthy worry in today's execution from expansive scope of numerous vision-based observation applications in light of the fact that they exceedingly troublesome the item characterization assignment. A few shadow identification strategies have been accounted for in the writing amid the most recent years. They are for the most part partitioned into two spaces. One more often than not works with static pictures, though the second one uses picture arrangements, to be specific video content. Regardless of the way that both cases can be similarly dissected, there is a distinction in the application field. The main case, shadow identification strategies can be misused to get extra geometric and semantic signs about shape and position of its throwing article ('shape from shadows') and the restriction of the light source. While in the second one, the primary reason for existing is normally change discovery, scene coordinating or reconnaissance (for the most part in a foundation subtraction connection). In our examination we have fundamentally focusssed on the identification of shadow from the facilitating so as to move article through a video observation test multi-wavelet choice and client configurable difference parameters. In our test client can pick the diverse wavelets and change parameters. Edge model based super determination technique is utilized to improve results. Additionally the impact of advanced watermarking is concentrated on for the super-determined VOP(Video articles planes). Various experiments have been proposed and figured out a best system for video reconnaissance application. Our proposed super determination (SR) system gives preferred results over bilinear and bi-cubic routines

Improved Coral Reef Images Segmentation using Modified JSEG Algorithm

Underwater coral reef image segmentation suffers from various challenges due to various factors especially variation in illumination, different water turbidity, different water depth, variation in color, texture and shape of the coral reef species. In this paper, we modified an original automatic color image segmentation called JSEG to enable better coral reef segmentation process. The modification involves the substitution of General Lloyd Algorithm and agglomerative algorithm in the original JSEG version with the k-means algorithm. In addition, the newly modified JSEG algorithm process image in L*a*b color space to provide better processing of underwater image color property while k-means algorithm is used to segment the color within the specified cluster number. The experimental results showed that the modified JSEG algorithm could segment the coral reefs better than the original JSEG algorithm

Unsupervised colour image segmentation by low-level perceptual grouping

This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by human

Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors

In today’s fast paced world, as the number of stations of television programming offered is increasing rapidly, time accessible to watch them remains same or decreasing. Sports videos are typically lengthy and they appeal to a massive crowd. Though sports video is lengthy, most of the viewer’s desire to watch specific segments of the video which are fascinating, like a home-run in a baseball or goal in soccer i.e., users prefer to watch highlights to save time. When associated to the entire span of the video, these segments form only a minor share. Hence these videos need to be summarized for effective presentation and data management. This thesis explores the ability to extract highlights automatically using MPEG-7 features and hidden Markov model (HMM), so that viewing time can be reduced. Video is first segmented into scene shots, in which the detection of the shot is the fundamental task. After the video is segmented into shots, extraction of key frames allows a suitable representation of the whole shot. Feature extraction is crucial processing step in the classification, video indexing and retrieval system. Frame features such as color, motion, texture, edges are extracted from the key frames. A baseball highlight contains certain types of scene shots and these shots follow a particular transition pattern. The shots are classified as close-up, out-field, base and audience. I first try to identify the type of the shot using low level features extracted from the key frames of each shot. For the identification of the highlight I use the hidden Markov model using the transition pattern of the shots in time domain. Experimental results suggest that with reasonable accuracy highlights can be extracted from the video