Pengenalan Objek Pada Computer Vision Dengan Pencocokan Fitur Menggunakan Algoritma SIFT Studi Kasus: Deteksi Penyakit Kulit Sederhana

Human vision can do amazing things such as recognizing people or objects, navigating through obstacles, recognizing the mood in a scene, and imagining stories. To do mimicry of the human vision, the computer requires a sensor that functions like the human eye and a computer program that serves as a data processor from the sensor. Computer vision is the science that uses image processing to make decisions based on images obtained from sensors. In other words, computer vision aims to build an intelligent machine that can "see". Computer vision can be used to detect skin diseases, for example, to detect disease Shingles (Herpes Zoster), Hives (Urticaria), Psoriasis, Eczema, Rosacea, Cold Sores (Fever Blisters), Rash, Razor Bumps, Skin Tags, Acne, Athlete's Foot, moles, Age or Liver Spots, Pityriasis Rosea, Melasma (Pregnancy Mask), Warts, and Seborrheic keratoses. Prewitt, Sobel, Roberts, and Canny operator are used to detect the edges of one or more objects. Then the results will be match with the results of edge detection image data base to determine the type of disease using Scale invariant Feature Transform (SIFT) algorithm. Skin Disease Detection Expert System will be implemented with C++ programming language, IDE MS Visual Studio 2010 and OpenCV 2.4 library. Keywords— computer vision, edge detection, SIFT algorithm, skin diseas

Kesesuaian Teknis Rasio Gaya Apung (Buoyance Force) Dan Gaya Tenggelam (Sinking Force) Pada Purse Seine Tipe Waring Di Tpi Sendang Sikucing, Kabupaten Kendal

Pukat cincin tipe waring ada 2 jenis yaitu webing PA (Polyamide) dengan sebutan lokal “sibolga”, sedangkan webing PE (Polyethylen) atau sebutan lokalnya “waring”. Penelitian ini bertujuan untuk Mengetahui, dan Menganalisis karakteristik bentuk dan konstruksi Purse Seine dengan webing PE “waring” dan webing PA “sibolga” di kabupaten kendal. Menganalisis kesesuaian teknis rasio gaya apung (buoyance force) dan gaya tenggelam (sinking force) pukat cincin (Purse Seine) tipe waring di kabupaten kendal. Penelitian ini akan dilaksanakan pada bulan Juli – Oktober 2014, dengan mengambil lokasi di TPI Sendang Sekucing, Kabupaten Kendal. Metode penelitian yang digunakan adalah metode deskripsi survei dengan simple random sampling. Metode pengumpulan data dengan cara Observasi, wawancara, dan dokumentasi. Metode analisis data menggunakan analisis karakteristik bentuk, menganalisis karakteristik konstruksi, menghitung berat komponen, menghitung gaya apung (buoyance force) dan gaya tenggelam (sinking force). Gaya apung Purse Seine yang ideal adalah sama dengan 1,5 – 2 kali jumlah gaya tenggelamnya. Gaya tenggelam terbesar dihasilkan oleh pemberat timah pada webing PE, pada webing dari PA yang terberat adalah bahan webing itu sendiri. Rasio gaya apung dan gaya tenggelam 24 unit yang mengguanakan webing PE dan 11 unit yang menggunakan webing PA, 1 unit yang memiliki rasio sesuai dengan prado, webing dari bahan PE meiliki rasio kurang dari 1,5. Webing dengan PA memiliki rasio lebih dari 2,0. Purse Seine waring type there are 2 types of waring PA (Polyamide) with local designation "sibolga", while webing PE (Polyethylen) or local designation "waring". buoyancy Purse Seine ideal is equal to 1.5 to 2 times the amount of force the sinking. This study aims to identify and analyze the characteristics of the shape and construction of the Purse Seine small pelagic with webing PE "waring" and webing PA "Bolga" in the district kendal and analyze the suitability of the technical ratio of buoyancy (buoyance force) and style sink (sinking force) Purse Seine (Purse Seine) type waring in kendal district. This study will be conducted in July-October 2014 took place in Spring PPI Sekucing, Kendal. The method used is a survey with a description of the method of simple random sampling. Methods of data collection by observation, interviews, and documentation. Methods of data analysis using Analyzing characteristics, Analyzing the characteristics of construction, heavy components Counting, Counting buoyancy (buoyance force) and the sink (sinking force). The method used is the description of the survey method with simple random sampling. Methods of data collection by observation, interview, and documentation. Methods of data analysis using the analysis of the characteristics of shape, to analyze the characteristics of construction, Calculating the weight of components, Calculating buoyancy (buoyance force) and style sink (sinking force). The buoyance force is generated by buoys, rope - rigging, srampat, and webbing PE (Polyethylen). The sink style produced by lead weights on webing PE and PA webing of the toughest is webing material itself. Ratio of buoyancy and style sank 24 units mengguanakan webing PE and 11 units that use webing PA, 1 unit has a ratio according to Prado, webing of PE materials meiliki ratio less than 1.5. Webing with PA have a ratio of more than 2.0

Seeing Tree Structure from Vibration

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://tree.csail.mit.edu

Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page