Enhancement of Over-Exposed and Under-Exposed Images Using Hybrid Gamma Error Correction Sigmoid Function

The demands to improve the visibility quality of the captured images in extremes lighting conditions have emerged increasingly important in digital image processing. The extremes conditions are when there is lack of reasonable lightnings termed as underexposed and too much of light termed as overexposed. The popular enhancement technique currently used is the contrast enhancement through contrast stretching, histogram equalization, homomorphic filtering and contrast adjustment. The adjustments are to transform the less useful images to more meaningful images when the post image processing operations are carried out. This thesis is motivated to deal with the problems concerning image capturing in these two extremes conditions. The sigmoid function is used to adjust the contrast with two controlling parameters. The parameters adjust the contrast locally and globally. The gamma function is commonly used to correct the non-linear error in the images due to the camera lenses. This thesis combines the functions' properties and developed a hybrid algorithm to improve the quality of the poorly captured images by adjusting the contrast and compensating the gamma error. The sigmoid and gamma function are coded in MATLAB 6.0 in which testes are made over the selected images. The sample images are taken using different type of cameras transformed to grayscaled input images. The luminosities of the surroundings are also measured using a light meter. The derivations of the parameters' ranges are done by calculating the root mean square error or the standard deviation. The suggested ranges are used in the hybrid system which has two variants, Variant I and Variant 11. The first variant, combines the sigmoid function inside the gamma compensation function while the second variant combines the gamma compensation function inside the sigmoid function. Based on the test results, the proposed algorithm significantly improves the contrast of the images. For the underexposed image samples, the percentages of the intensity lesser than 0.1 decreases as more of the intensities reside at higher values. For the overexposed image samples, the percentages of intensity greater than 0.9 decreases as more of the intensities reside at lower values. With the suggested range deduced, the images are contrast enhanced with the reduction of percentage of pixels residing he intensity less than 0.1 and greater than 0.9. The comparative analyses are made by comparing the suggested hybrid system with the existing adaptive homomorphic filtering, adaptive histogram equalization and adaptive contrast enhancement

Study on the relationship between the workload and current implemented learning system of the students in the Dept. of Computer and Communication Systems Engineering, UPM

Outcomes-based education (OBE) system is a motivated and reformed education system. OBE has been implemented in Bachelor of Engineering (B. Eng) programme offered by the Faculty of Engineering, Universiti Putra Malaysia since 2006. The learning process focuses on measuring the students' outcomes empirically. To contrast to OBE, the conventional learning system is primarily focuses on the resources available to students and to lecturers. In this paper, we present the relationship between the workload and the current implemented OBE learning system. Suggestions to improve the current OBE learning systems are also presented in order to enhance the learning method to benefit every entity involve in this system A survey was conducted to the student in the Department of Computer and Communication Systems Engineering. Most of the respondents do understand and aware about the concept of OBE since it was implemented. From the survey, more than 80 % of the respondents aware the implementation of OBE in the department and 75% of them agree that OBE system has increased their workload

Continuous-grouped-self-learning: in the perspective of lecturers, tutors and laboratory instructors

This paper presents the perception of lecturers, tutors and lab instructors towards the implemented Continuous-Group-Self-Learning (CGSL) in the Department of Computer and Communication System Engineering (CCSE), Universiti Putra Malaysia. This innovative system introduces mock teaching and student-lecturer role as a technique of delivery. The system ensures a continuous group work and the students are learning with class-oriented problem-based learning (CO-PBL) instead of seasonal project oriented problem-based learning (PO-PBL). The radical change in the assessment by adopting mock teaching oriented assessment (MTOA) has given a new definition to assess the student thoroughly. 49 respondents have taken part in this study, in which 30 of them are lecturers, 8 are tutors and 11 are laboratory instructors who currently active serving in the department. In general, 56% of the respondent do not agree this learning system shifted the teaching job to the students and 56.55% of them disagree this approach is a burdensome to the students who are undergoing this learning style. This system in fact a catalyst that urges the lecturers, tutors and lab instructors to enhance themselves in order to cope up with the ‘knowledge demand’ from the student when 82.1% of the respondents agree to be more knowledgeable as compared to conventional teaching method

Implementation of continuous-grouped-self-learning (CGSL) system in engineering education

This paper presents a new method in engineering education called a continuous-grouped-self-learning (CGSL) system. In general, based on feedbacks acquired from the student at the end of the implementation, students better understanding and appreciation to the courses been taught. Out of 60 students 71.7% admitted that this learning system has improved their study style and knowledge acquisition, which then agreed to be implemented in future. The observation shows that this system increases the appreciation to the knowledge better than conventional method. Apart from that the system also produced better students in term of responsibility, self-confidence, competitiveness, group work, and knowledge sharing. This is realized with the implementation of mock teaching assessment