Analisis Segmentasi Image menggunakan Metode Iteratively Mean Shift Filtering Analysis of Segmentation Image by using Iteratively Mean Shift Filetring Method

ABSTRAKSI: Segmentasi citra merupakan proses penting yang dapat menentukan kualitas pengenalan pola. Semakin baik hasil segmentasi maka akan sangat membantu dan mempermudah pengenalan pola. Oleh karena itulah pada Tugas Akhir kali ini dilakukan proses pensegmentasian citra agar nantinya hasil dari proses segmentasi menggunakan metode iteratively mean shift filtering dapat digunakan pada proses pengenalan pola. Metode ini dipilih karena pensegmentasian citra menggunakan metode iterativey mean shift filtering dilakukan secara tepat berdasarkan warna yang dimiliki citra yang bersangkutan. Sehingga untuk setiap intensitas warna yang berbeda akan dikelompokkan menjadi daerah yang berbeda pula.Berdasarkan hasil pengujian, tingkat akurasi yang dihasilkan dari penerapan metode ini tergolong sangat baik untuk citra buatan, namun untuk citra foto dan citra medis metode ini masih cukup baik. Hal ini dapat diketahui berdasarkan nilai PSNR yang dihasilkan. Semakin besar parameter spatial radius dan color distance yang digunakan menyebabkan semakin rendahnya kualitas dan performansi hasil segmentasi.Kata Kunci : segmentasi citra, iteratively mean shift filtering, spatial domain,color distance, PSNRABSTRACT: Image segmentation is an important process which able to determine the quality of pattern recognition. If result of segmentation image is good then the result of patern recognition will be better too. Therefore, in this Final Duty more be focussed about segmentation process so that the result from segmentation process using iteratively mean shift filtering method can be used at pattern recognition process. This method is selected because this method can conduct image segmentation pursuant to color of image, so that every different colour intensity image will be grouped to become different area.Based on a examination result, yielded accuration level of applying of this method pertained very good to image made in, but for image photograph and medical image, this method still good enough. This matter can know pursuant to value of PSNR. A big color distance and spatial radius can make a bad result.Keyword: image segmentation, iteratively mean shift filtering, spatial domain,color distance, PSN

CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images

Medical image compression is a widely studied field of data processing due to its prevalence in modern digital databases. This domain requires a high color depth of 12 bits per pixel component for accurate analysis by physicians, primarily in the DICOM format. Standard raster-based compression of images via filtering is well-known; however, it remains suboptimal in the medical domain due to non-specialized implementations. This study proposes a lossless medical image compression algorithm, CompaCT, that aims to target spatial features and patterns of pixel concentration for dynamically enhanced data processing. The algorithm employs fractal pixel traversal coupled with a novel approach of segmentation and meshing between pixel blocks for preprocessing. Furthermore, delta and entropy coding are applied to this concept for a complete compression pipeline. The proposal demonstrates that the data compression achieved via fractal segmentation preprocessing yields enhanced image compression results while remaining lossless in its reconstruction accuracy. CompaCT is evaluated in its compression ratios on 3954 high-color CT scans against the efficiency of industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its reconstruction performance is assessed with error metrics to verify lossless image recovery after decompression. The results demonstrate that CompaCT can compress and losslessly reconstruct medical images, being 37% more space-efficient than industry-standard compression systems.Comment: (8/24/2023) v1a: 16 pages, 9 figures, Word PD

Graph-based analysis of textured images for hierarchical segmentation

International audienceThe Texture Fragmentation and Reconstruction (TFR) algorithm has been recently introduced to address the problem of image segmentation by textural properties, based on a suitable image description tool known as the Hierarchical Multiple Markov Chain (H-MMC) model. TFR provides a hierarchical set of nested segmentation maps by first identifying the elementary image patterns, and then merging them sequentially to identify complete textures at different scales of observation. In this work, we propose a major modification to the TFR by resorting to a graph based description of the image content and a graph clustering technique for the enhancement and extraction of image patterns. A procedure based on mathematical morphology will be introduced that allows for the construction of a color-wise image representation by means of multiple graph structures, along with a simple clustering technique aimed at cutting the graphs and correspondingly segment groups of connected components with a similar spatial context. The performance assessment, realized both on synthetic compositions of real-world textures and images from the remote sensing domain, confirm the effectiveness and potential of the proposed method

Interaction between high-level and low-level image analysis for semantic video object extraction

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Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain

In this paper, we show that we can apply probabilistic spatiotemporal macroblock filtering (PSMF) and partial decoding processes to effectively detect and track multiple objects in real time in H.264|AVC bitstreams with stationary background. Our contribution is that our method cannot only show fast processing time but also handle multiple moving objects that are articulated, changing in size or internally have monotonous color, even though they contain a chaotic set of non-homogeneous motion vectors inside. In addition, our partial decoding process for H.264|AVC bitstreams enables to improve the accuracy of object trajectories and overcome long occlusion by using extracted color information.Comment: SPIE Real-Time Image and Video Processing Conference 200

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning