An Interactive Algorithm for Image Smoothing and Segmentation

This work introduces an interactive algorithm for image smoothing and segmentation. A non-linear partial differential equation is employed to smooth the image while preserving contours. The segmentation is a region-growing and merging process initiated around image minima (seeds), which are automatically detected, labeled and eventually merged. The user places one marker per region of interest. Accurate and fast segmentation results can be achieved for gray and color images using this simple method

Analisis dan Implementasi Segmentasi Citra Berwarna Menggunakan Algoritma JSEG Analysis and Implementation of Color Image Segmentation Using JSEG Algorithm

ABSTRAKSI: Segmentasi citra adalah proses membagi citra digital menjadi beberapa region (kumpulan piksel). Tujuan dari segmentasi citra adalah menyederhanakan atau mengubah representasi sebuah citra sehingga lebih mudah untuk dianalisisBeberapa algoritma populer dan klasik dalam proses segmentasi citra berdasarkan warna diantaranya algoritma watersheed, region growing, splitting and merging dan lain-lain. Melengkapi algoritma klasik yang sudah ada ditambah dengan karakteristik yang khas, algoritma J-Segmentation (JSEG) muncul sebagai algoritma baru segmentasi citra berwarna. Algoritma ini diperkenalkan oleh Yining Deng, B. S Manjunath dan Hyundon Shin pada tahun 1999, dan masih dikembangkan hingga saat ini dalam bidang image retrieval.Algoritma JSEG diklaim sebagai algoritma yang baik yang dapat diterapkan pada berbagai macam tipe file citra. Namun sejauh ini belum ada pengujian ketahanan (robustness) algoritma ini terhadap gangguan berupa noise ataupun efek blur sebagai salah satu bukti pendukung terhadap klaim bahwa algoritma JSEG adalah algoritma yang baik.Dalam Tugas Akhir ini akan dilakukan analisis pengaruh parameter threshold kuantisasi warna dan penggabungan region terhadap hasil segmentasi citra menggunakan algoritma JSEG. Selain itu juga akan dilakukan uji ketahanan segmentasi citra dengan algoritma JSEG pada citra berwarna dengan tingkat gangguan noise dan blur yang berbeda.Kata Kunci : segmentasi, citra, JSEG, noise, blur, robustness.ABSTRACT: Image Segmentation is a process to divide an image to several region. The purpose of Image segmentation is to simplify or to change an image representation so the image is easier for image analysis.Some of popular and classic image segmentation algorithm are watersheed algorithm, region growing, splitting and merging etc. J-Segmentation (JSEG) algorithm appeared as new color image segmentation algorithm. This algorithm introduced by Yining Deng, B. S Manjunath and Hyundon Shin at 1999, and still developing until now in image retrieval purpose.JSEG Algorithm is claimed as good algorithm that can be applied on several type of image file. But until now, there is no robustness testing to this algorithm about handling noise and blur to support the claiming that JSEG is good image color segmentation algorithmThis final project analyse the effect of parameter threshold color quantization and region merging using JSEG Algorithm to image segmentation result and also analyse JSEG robustness towards color image with several level noises and blurs.Keyword: segmentation, image, JSEG, noise, blur, robutsnes

Region based analysis of video sequences with a general merging algorithm

Connected operators [4] and Region Growing [2] algorithms have been created in different context and applications. However, they all are based on the same fundamental merging process. This paper discusses the basic issues of the merging algorithm and presents different applications ranging from simple frame segmentation to video sequence analysis.Peer ReviewedPostprint (published version

Depth map compression via 3D region-based representation

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an efficient encoding while preserving the 3D characteristics of the scene. The 3D planes open up the possibility to code multiview images with a unique representation.Postprint (author's final draft

SIMULASI DAN ANALISIS ALGORITMA JSEG UNTUK PEMISAHAN OBJEK BANGUNAN BERDASARKAN CITRA DARI GOOGLE EARTH

ABSTRAKSI: Di zaman sekarang, Google Earth adalah salah satu solusi terbaik manusia dalam mencari rute jalan dan lokasi sebuah tempat, misalnya mencari bangunan sebuah kantor, hotel dan mall. Pada kawasan tertentu juga tersedia bentuk-bentuk bangunan dalam format tiga dimensi. Tapi pada Google Earth juga terdapat gambar gunung, laut, sawah, lapangan, pohon dan lainnya sehingga pencarian bangunan semakin sulit dilakukan karena terhambat oleh objek-objek tersebut.Pada tugas akhir ini dilakukan pemisahan objek bangunan dengan background pada citra yang dicuplik dari Google Earth. Metode yang digunakan adalah algoritma JSEG. Algoritma JSEG adalah proses segmentasi citra dengan dua tahap yaitu tahap kuantisasi warna dan segmentasi spasial. Kuantisasi warna mempunyai dua proses yaitu proses peer group filtering dan generalized lloyd algorithm. Sedangkan pada tahap segmentasi spasial terdapat proses region growing dan region merging.Hasil simulasi segmentasi citra telah berhasil dilakukan. Dengan parameter threshold terbaik untuk PGF adalah 2. Threshold untuk GLA adalah 10. Threshold region merging adalah 20. Nilai MOS rata-rata untuk semua citra adalah 3,886667. Dan disamping itu juga secara tidak langsung membuktikan bahwa algoritma JSEG menghasillkan segmentasi citra yang cukup baik pada citra berdasarkan objek.Kata Kunci : Segmentasi Citra, Google Earth, Algoritma JSEG, ThresholdABSTRACT: In the current era , Google Earth is one of the best solution for human to find street and the location of a place , such as looking for an office building, hotel and mall . In certain areas is also available in the building forms a three-dimensional format . But on Google Earth also shows the image of mountains, ocean, fields, field, trees and other buildings so the search more difficult because it is hampered by such objects .This final project will be to separate objects with background buildings based on Google Earth images . The method used is JSEG algorithm . JSEG algorithm is image segmentation process with two phases: color quantization and spatial segmentation. Color quantization has two processes, the first is process of peer group filtering and the second is generalized lloyd algorithm. At the same time, spatial segmentation also has two processes, region growing and region merging.Simulation results of image segmentation has been done successfully. With the best threshold PGF parameter is 2. Threshold for GLA is 10. Region merging threshold is 20. MOS values average for all images is 3.886667. And besides that it also indirectly proves that the JSEG algorithm quite good image segmentation based on object.Keyword: Image Segmentation , Google Earth , JSEG Algorithm, Threshol

Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

An improved image segmentation algorithm for salient object detection

Semantic object detection is one of the most important and challenging problems in image analysis. Segmentation is an optimal approach to detect salient objects, but often fails to generate meaningful regions due to over-segmentation. This paper presents an improved semantic segmentation approach which is based on JSEG algorithm and utilizes multiple region merging criteria. The experimental results demonstrate that the proposed algorithm is encouraging and effective in salient object detection