Adhi Susanto

A Comparative Experiment of Several Shape Methods in Recognizing Plants

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.

FOLIAGE PLANT RETRIEVAL USING POLAR FOURIER TRANSFORM , COLOR MOMENTS AND VEIN FEATURES

This paper proposed a method that combines Polar Fourier Transform, color moments, and vein features to retrieve leaf images based on a leaf image. The method is very useful to help people in recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns in the leaf. Therefore, the colors and its patterns are information that should be counted on in the processing of plant identification. To compare the performance of retrieving system to other result, the experiments used Flavia dataset, which is very popular in recognizing plants. The result shows that the method gave better performance than PNN, SVM, and Fourier Transform. The method was also tested using foliage plants with various colors. The accuracy was 90.80% for 50 kinds of plants.

Inertial Measurement Unit using multigain accelerometer sensor and gyroscope sensor

The employment of Inertial Measurement Unit (IMU) to measure the acceleration and angular rate in three axes is an important part of the navigation control system. The motion of an object (e.g. a rocket) has very wide range of acceleration at the forward axis. To obtain optimum sensitivities of all sensors, multigain accelerometer sensors should be looked into. For this a three axes accelerometer sensor (MMA7260Q) and three angular rate sensor (ADXRS150) on the three axes.

Real time hand gesture movements tracking and recognizing system

Human computer interaction has a long history to become more intuitive. For human being, gesture of different kind is one of the most intuitive and common communication. However, vision-based hand gesture recognition is a challenging problem, which is involved complex computation, due to high degree of freedom in human hand. In this paper, we use hand gesture captured by web-cam instead of mice, for natural and intuitive human-computer interaction. Skin detections method is used to create a segmented hand image and to differentiate with the background. A contours and convex hull algorithm is used to recognize hand area as well as the number of fingertip of hand gesture image to be mapped with button. Moreover, for detection of hand gesture motion, we use Lucas-Kanade pyramidal algorithm. The result shows that this system can operate well so we can interact with computer using our hand gesture instead using a mouse.

Tech review: Game platform for upgrading counting ability on preschool children

One of difficulties in learning mathematics (counting) can be overcome by providing a relaxed and fun learning for preschoolers. Games can be used as an alternative solution. This study was conducted as a pilot project for reviewing the Kodu, Unity 3D and Construct 2 game platform for this purpose. The method used is the classification, review/evaluation, prototyping and analysis. Tech Review of these game platforms will be discussed as a result in this paper.

Preliminary design of static indonesian sign language recognition system

Human computer interaction has a long history to become more intuitive. For human being, especially for the deaf, gesture of different kind is one of the most intuitive and common communication. In this paper we focus on creating a system to identified and translate hand gesture pose to Indonesian alphabets. Skin detections method is used to create a segmented hand image and to differentiate with the background. A contours is used to localize hand area. SIFT algorithm in advanced, were used to recognize the signed gesture. The result shows that this system can operate well in translated hand gesture image of sign into Indonesian alphabets.

Real time static hand gesture recognition system prototype for Indonesian sign language

Sign language uses gestures instead of speech sound to communicate. However, it is rare that the normal people try to learn the sign language for interacting with deaf people. Therefore, the need for a translation from sign language to written or oral language becomes important. In this paper, we propose a prototype system that can recognize the hand gesture sign language in real time. We use HSV (Hue Saturation Value) color space combined with skin detection to remove the complex background and create segmented images. Then a contour detection is applied to localize and save hand area. Further, we use SURF algorithm to detect and extract key point features and recognize each hand gesture sign alphabet by comparing with these user image database. Based on the experiments, the system is capable to recognize hand gesture sign and translate to Alphabets, with recognize rate 63 % in average.

Computer Aided Diagnosis for lung tuberculosis identification based on thoracic X-ray

This paper presents research on lung tuberculosis (TB) identification by using computer. This research was attempt to reduce patient waiting time in receiving X-ray diagnosis result on lung TB disease, due to mismatch ratio of radiologic experts to the number of patient, especially from remote areas in Indonesia. We used textural features calculated by computer to be used as descriptor in classifying image as TB or non-TB. We used statistical features of image histogram by calculates five features: mean, standar deviation (std), skewness, kurtosis, and entropy. These features were calculated from ROI images using pre defined ROI shape from thresholding method. Features calculated was then reduced down to one principal feature using Principal Componen Analysis (PCA) method. Finally, we used Mahalanobis distance classifier as classifier method based on one principal feature as descriptor. This research results show that it was possible to classify TB and non-TB image based on statistical feature on image histogram.

Design of Automated Moving Stage with Adaptive Focus System to Support Microscopy Image Stitching

Image stitching is a method to obtain high-resolution images through compositing several image elements using invariant pattern recognizing and matching between each image elements involved. This method can remove resolution restriction in digital microscopy imaging. This study aims to design automated stitching with adaptive focus mechanism to achieve high-quality final stitched image. We attach three-axis microscopic actuator into Olympus CX-21 light microscope. The actuator has been programmed to support image elements capture with adaptive focus ability to compensate focus plane changes due to unevenness object surface, inhomogeneous slide thickness and imperfection of preparation holder. Testing results show automated moving stage has horizontal step resolution 0.198±0.001 μm/step with hysteresis 5.99±1.09 μm and vertical step resolution 0.197±0.004 μm/step with hysteresis 2.36±1.28 μm at maximum speed 3,675μm/sec in 16 sub-division microstep setting value. Automated moving stage has also linear response with R2=0.999. Adaptive focus testing show satisfied optimum objective height locking stability using identical factor ε=95% for every objective application below 100x. Adaptive focus success stabilizes Zopt for every image element to generate final stitched image with homogenous focus value.

Thoracic X-ray features extraction using thresholding-based ROI template and PCA-based features selection for lung TB classification purposes

This paper describes the results of research in finding the X-ray image features for the development of computer applications for identification of lung tuberculosis (TB) disease. We used statistical features of image histogram by calculates five features: mean, standar deviation (std), skewness, kurtosis, and entropy. These features were calculated from ROI images using pre-defined ROI shape from thresholding method. Average of trainer images was used in designing ROI shapes template using thresholding method. Features calculated was then reduced down to one principal feature using Principal Componen Analysis (PCA) method. This selected feature was to be used as descriptor in classifying image as TB or non-TB. We used Mahalanobis distance classifier to examined descriptor performance in image classification process. Image classification results show that features extraction can be done effectively using combination of thresholding-based ROI template and PCA (Principle Component Analysis) methods.