Extending the Teknomo-Fernandez Background Image Generation Algorithm on the HSV Colour Space

Background subtraction, a procedure required in many video analysis applications such as object tracking , is dependent on the model background image. One efficient algorithm for background image generation is the Teknomo-Fernandez (TF) Algorithm, which uses modal values and a tournament-like strategy to produce a good background image very quickly. A previous study showed that the TF algorithm can be extended from the original 3 frames per tournament (T F 3) to T F 5 and T F 7, resulting in increased accuracies at a cost of increased processing times. In this study, we explore extending the T F 3, T F 5 and T F 7 from the original RGB colour space to the HSV colour space. A ground truth model background image for HSV was also developed for comparing the performances between the TF implementations on the RGB and HSV channels. The results show that the TF algorithm generates accurate background images when implemented on the HSV colour space. However, the RGB implementations still exhibit higher accuracies than the corresponding HSV implementations. Finally, background subtraction was applied on the HSV generated background images. A comparison with other promising baseline techniques validates the competitiveness of the TF algorithm implemented on HSV channels

Machine Vision-based Obstacle Avoidance for Mobile Robot

Obstacle avoidance for mobile robots, especially humanoid robot, is an essential ability for the robot to perform in its environment. This ability based on the colour recognition capability of the barrier or obstacle and the field, as well as the ability to perform movements avoiding the barrier, detected when the robot detects an obstacle in its path. This research develops a detection system of barrier objects and a field with a colour range in HSV format and extracts the edges of barrier objects with the FindContoure method at a threshold filter value. The filter results are then processed using the Bounding Rect method so that the results are obtained from the object detection coordinate extraction. The test results detect the colour of the barrier object with OpenCV is 100%, the movement test uses the processing of the object's colour image and robot direction based on the contour area value> 12500 Pixels, the percentage of the robot making edging motion through the red barrier object is 80% and the contour area testing <12500 pixel is 70% of the movement of the robot forward approaching the barrier object

Machine Vision-based Obstacle Avoidance for Mobile Robot

Obstacle avoidance for mobile robots, especially humanoid robot, is an essential ability for the robot to perform in its environment. This ability based on the colour recognition capability of the barrier or obstacle and the field, as well as the ability to perform movements avoiding the barrier, detected when the robot detects an obstacle in its path. This research develops a detection system of barrier objects and a field with a colour range in HSV format and extracts the edges of barrier objects with the FindContoure method at a threshold filter value. The filter results are then processed using the Bounding Rect method so that the results are obtained from the object detection coordinate extraction. The test results detect the colour of the barrier object with OpenCV is 100%, the movement test uses the processing of the object's colour image and robot direction based on the contour area value> 12500 Pixels, the percentage of the robot making edging motion through the red barrier object is 80% and the contour area testing <12500 pixel is 70% of the movement of the robot forward approaching the barrier object

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests

Machine Vision-based Obstacle Avoidance for Mobile Robot

Obstacle avoidance for mobile robots, especially humanoid robot, is an essential ability for the robot to perform in its environment. This ability based on the colour recognition capability of the barrier or obstacle and the field, as well as the ability to perform movements avoiding the barrier, detected when the robot detects an obstacle in its path. This research develops a detection system of barrier objects and a field with a colour range in HSV format and extracts the edges of barrier objects with the FindContoure method at a threshold filter value. The filter results are then processed using the Bounding Rect method so that the results are obtained from the object detection coordinate extraction. The test results detect the colour of the barrier object with OpenCV is 100%, the movement test uses the processing of the object's colour image and robot direction based on the contour area value&gt; 12500 Pixels, the percentage of the robot making edging motion through the red barrier object is 80% and the contour area testing &lt;12500 pixel is 70% of the movement of the robot forward approaching the barrier object

Mobile Application for Identification of Coffee Fruit Maturity using Digital Image Processing

Indonesia is an agricultural country that relies on the agricultural sector and is well known in producing various plantation commodities, one of which is coffee. Coffee is a leading export commodity developed in Indonesia. Community coffee plantations play an important role because most of the coffee production comes from community plantations. However, the condition of community coffee plantations can be said to be still hampered, due to the quality of coffee is still relatively low. It is caused by coffee fruit sorting, which is still done conventionally. The conventional sorting process of coffee fruits is still carried out with the help of operator knowledge, so the level of operator knowledge dramatically influences the results of sorting. The ease of sorting coffee ripeness can be done by implementing a mobile application using digital image processing. Techniques used in digital image processing are the HSV color space to get color features of coffee fruit and the K-Nearest Neighbor (KNN) classification method to classify coffee fruit ripeness. The results of the identification are in the form of ripe, half-ripe, or unripe fruits. The mobile application of this research has two main features, namely training data feature and non real-time identification feature. The results of the testing conducted resulted in an accuracy rate of 95.56% with the best membership value (k) of 3

Design of a High-Speed Architecture for Stabilization of Video Captured Under Non-Uniform Lighting Conditions

Video captured in shaky conditions may lead to vibrations. A robust algorithm to immobilize the video by compensating for the vibrations from physical settings of the camera is presented in this dissertation. A very high performance hardware architecture on Field Programmable Gate Array (FPGA) technology is also developed for the implementation of the stabilization system. Stabilization of video sequences captured under non-uniform lighting conditions begins with a nonlinear enhancement process. This improves the visibility of the scene captured from physical sensing devices which have limited dynamic range. This physical limitation causes the saturated region of the image to shadow out the rest of the scene. It is therefore desirable to bring back a more uniform scene which eliminates the shadows to a certain extent. Stabilization of video requires the estimation of global motion parameters. By obtaining reliable background motion, the video can be spatially transformed to the reference sequence thereby eliminating the unintended motion of the camera. A reflectance-illuminance model for video enhancement is used in this research work to improve the visibility and quality of the scene. With fast color space conversion, the computational complexity is reduced to a minimum. The basic video stabilization model is formulated and configured for hardware implementation. Such a model involves evaluation of reliable features for tracking, motion estimation, and affine transformation to map the display coordinates of a stabilized sequence. The multiplications, divisions and exponentiations are replaced by simple arithmetic and logic operations using improved log-domain computations in the hardware modules. On Xilinx\u27s Virtex II 2V8000-5 FPGA platform, the prototype system consumes 59% logic slices, 30% flip-flops, 34% lookup tables, 35% embedded RAMs and two ZBT frame buffers. The system is capable of rendering 180.9 million pixels per second (mpps) and consumes approximately 30.6 watts of power at 1.5 volts. With a 1024×1024 frame, the throughput is equivalent to 172 frames per second (fps). Future work will optimize the performance-resource trade-off to meet the specific needs of the applications. It further extends the model for extraction and tracking of moving objects as our model inherently encapsulates the attributes of spatial distortion and motion prediction to reduce complexity. With these parameters to narrow down the processing range, it is possible to achieve a minimum of 20 fps on desktop computers with Intel Core 2 Duo or Quad Core CPUs and 2GB DDR2 memory without a dedicated hardware

FUZZY K-NEAREST NEIGHBOR PADA KLASIFIKASI KEMATANGAN CABAI BERDASARKAN FITUR HSV CITRA

Cabai merupakan salah satu bahan masakan yang disukai masyarakat Indonesia. Salah satu cabai yang banyak dimanfaatkan sebagi bahan masakan yaitu cabai rawit. Pada umumnya identifikasi kematangan cabai dilakukan secara manual berdasarkan warna. Metode manual dilakukan dengan pengamatan secara visual. Cara ini membutuhkan tenaga lebih banyak dalam memilah kematangan cabai, padahal persepsi manusia bisa berbeda-beda, hal ini meninbulkan ketidakkonsistenan hasil yang diperoleh. Berdasarkan permasalahan tersebut, penelitian ini dilakukan untuk proses klasifikasi kematangan cabai rawit. Ekstraksi ciri yang digunakan pada penelitian ini dengan menggunakan nilai HSV. Nilai ini diperoleh dari perhitungan nilai RGB citra. Sedangkan proses klasifikasi menggunakan metode k-nearest neighbor yang ditambahkan fuzzy dalam mencari keanggotaan kelas hasil klasifikasi. Metode ini kemudian disebut Fuzzy K-Nearest Neighbor. Pengujian yang dilakukan terhadap 60 data cabai rawit. Berdasarkan pengujian dengan hasil sesuai klasifikasi kelas sesungguhnya yaitu 15 cabai matang, 15 cabai mentah, 15 cabai setengah matang,dan  7 cabai busuk. Sedangkan hasil klasifikasi yang salah yaitu 8 cabai busuk. Dari pengujian tersebut diperoleh 52 data dengan klasifikasi sesuai dengan kelas aslinya. Dari hasil tersebut diperoleh  dengan akurasi sebesar 86,66%