Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine

Teh dapat dikenal kualitasnya melalui aroma yang dihasilkan. Penelitian klasifikasi teh menggunakan e-nose umumnya hanya mendeteksi kualitas aroma menggunakan general sensor gas. Namun, adanya redundansi fitur sensor dapat menyebabkan penurunan performa sistem e-nose. Oleh karena itu diperlukan sebuah sistem yang dapat menyeleksi fitur sehingga performa klasifikasi menjadi lebih optimal. Pada penelitian ini dibentuk sistem perangkat lunak yang mampu menyeleksi fitur untuk mengoptimalkan performa klasifikasi. Data input untuk sistem adalah respon sensor e-nose terhadap 3 kualitas teh hitam dengan jumlah sampel 300. Fitur yang diseleksi berupa sensor-sensor pada instrumen e-nose. Proses seleksi fitur dilakukan dengan pendekatan wrapper, algoritma ABC digunakan untuk seleksi fitur, kemudian hasil fitur yang terpilih dievalusi dengan klasifikasi menggunakan SVM. Hasil sistem ABC-SVM kemudian dibandingkan dengan sistem SVM tanpa seleksi fitur. Hasil penelitian menunjukkan bahwa dari 12 sensor e-nose, sensor yang paling mencirikan teh hitam kualitas 1-3 yaitu sensor TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 dan TGS 822. Sedangkan untuk sensor MQ-7, TGS 826 dan TGS 2610 merupakan sensor yang redundant pada sistem dikarenakan gas yang dideteksi oleh 3 sensor tersebut dapat diwakili oleh sensor lainnya. Dengan berkurangnya fitur menjadi 9, performa akurasi klasifikasi meningkat 16,7%

Applied of image processing technique on semi-auto count of skin spot

Skin is the biggest organ in the human body and works to separate the inner body part from outer environment. In the skin, there are sebaceous glands found inside the pores of the skin. They are at all over the body except for the palms of the hands and the feet soles. There are more sebaceous glands on the face and scalp than elsewhere. Sebaceous gland secretes an oily protective skin surface, sebum, which is against pathogens and also help to slow down the skin ageing process [1]. They can help to maintain the moisture of the skin. However, the sebaceous glands become overactive sometimes, thus, producing too much sebum and the pores can get clogged together with dead skin [2][3]. This will results in having blackheads along with other factors. Blackhead is one of an acne vulgaris type [4]. It is a small dark spots on the skin that sometimes hard to be seen under a naked eye. If the clogged pores infect the glands, the accumulated sebum may form a sac and slowly increase in size. Lack of sebum production can also provide unsatisfied result that could cause dry skin, which makes the skin, looks rough and dull

BL_Wiener Denoising Method for Removal of Speckle Noise in Ultrasound Image

Medical imaging techniques are extremely important tools in medical diagnosis. One of these important imaging techniques is ultrasound imaging. However, during ultrasound image acquisition process, the quality of image can be degraded due to corruption by speckle noise. The enhancement of ultrasound images quality from the 2D ultrasound imaging machines is expected to provide medical practitioners more reliable medical images in their patients’ diagnosis. However, developing a denoising technique which could remove noise effectively without eliminating the image’s edges and details is still an ongoing issue. The objective of this paper is to develop a new method that is capable to remove speckle noise from the ultrasound image effectively. Therefore, in this paper we proposed the utilization of Bilateral Filter and Adaptive Wiener Filter (BL_Wiener denoising method) for images corrupted by speckle noise. Bilateral Filter is a non-linear filter effective in removing noise, while Adaptive Wiener Filter balances the tradeoff between inverse filtering and noise smoothing by removing additive noise while inverting blurring. From our simulation results, it is found that the BL_Wiener method has improved between 0.89 [dB] to 3.35 [dB] in terms of PSNR for test images in different noise levels in comparison to conventional methods

Quantization Error Minimization by Reducing Median Difference at Quantization Interval Class

In this paper, a new technique to define the size of quantization interval is defined. In general, high quantization error will occur if large interval is used at a large difference value class whereas low quantization error will occur if a small interval is used at a large difference value class. However, the existence of too many class intervals will lead to a higher system complexity. Thus, this research is mainly about designing a quantization algorithm that can provide an efficient interval as possible to reduce the quantization error. The novelty of the proposed algorithm is to utilize the high occurrence of zero coefficient by re-allocating the non-zero coefficient in a group for quantization. From the experimental results provided, this new algorithm is able to produce a high compressed image without compromising with the image quality

Visualization on colour based flow vector of thermal image for movement detection during interactive session

Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine

Thresholding and quantization algorithms for image compression techniques: a review

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need

OTSUHARA-WATH Filter for Poisson Noise Removal in Low Light Condition Digital Image

Nowadays, the digital images are used widely due to the development of sophisticated technologies. The recent device that is very popular among its users related to digital images is smartphone. This is due to nowadays smartphone is embedded with its own camera that can capture digital images. Nevertheless, the digital image is easily exposed to various types of noise, especially the Poisson noise in low light condition. Therefore, this study aims to develop a new denoising technique for Poisson noise removal in low light condition digital images. This study proposes a denoising method named as OTSUHARAWATH Filter, which utilizes the Otsu Threshold, Kuwahara Filter and Wavelet Threshold. The proposed methods performance is evaluated based on the Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and visual inspection. The comparison between the proposed methods and the existing denoising methods is also performed. From the results of PSNR, MSE, computational time and visual inspection, it can be proven that the OTSUHARA-WATH Filter is able to reduce and smooth noise, while preserving the edges and fine details of the image at low and medium level of Poisson noise in comparison to the existing methods

The Effect on Compressed Image Quality using Standard Deviation-Based Thresholding Algorithm

In recent decades, digital images have become increasingly important. With many modern applications use image graphics extensively, it tends to burden both the storage and transmission process. Despite the technological advances in storage and transmission, the demands placed on storage and bandwidth capacities still exceeded its availability. Compression is one of the solutions to this problem but elimination some of the data degrades the image quality. Therefore, the Standard Deviation-Based Thresholding Algorithm is proposed to estimate an accurate threshold value for a better-compressed image quality. The threshold value is obtained by examining the wavelet coefficients dispersion on each wavelet subband using Standard Deviation concept. The resulting compressed image shows a better image quality with PSNR value above 40dB