Seungmin Lee

기존의 수동적인 헤어핀 권선 품질 검사는 효율적이지 않고 물리적 마찰로 인한 성능 저하를 일으킬 수 있다. 본 연구에서는 물리적 접촉이 없는 영상 인식 기반의 전기모터용 헤어핀 불량 검증 시스템을 제안한다. 헤어핀 권선의 품질을 검사하기 위하여 다양한 이미지 처리 기술들을 활용하여 영상에서 나타나는 헤어핀의 중요한 특징들을 분석한다. 본 논문에서는 카메라 캘리브레이션과 스케일링 팩터를 이용하여 헤어핀의 길이를 추정하여 미리 정해놓은 기준을 벗어나면 불량으로 검출하는 시스템을 제안한다. 또한 인공지능 기반의 신경회로망을 이용하여 헤어핀과 모터 고정자의 결합이 제대로 되지 않은 것을 검출하는 시스템을 제안한다.

For the technology development of super‐sized 110 inch 16K high resolution Panel, we first evaluated the process route, compared and analyzed the ADS, i‐ADS and HADS+ORG (organic film) three pixel display modes, and selected the HADS+ORG (organic film) display mode. On the basis of analyzing the load of Panel, We evaluated the driving capability from two aspects: GOA driver and Source driver. Next, on the existing products 110 "8K (similar charging rate) and 55" 8K (same PPI), the picture quality of 110 "16K was simulated and evaluated. Meanwhile, Array splicing exposure and CF splicing exposure were analyzed respectively, and the ultra‐high PPI splicing Mura was evaluated. By exploring the development of 110‐inch 16K technology, it lays a foundation for the design of future super‐sized high‐resolution products.

Seungmin Lee and Bongsoon Kang, Member, KIICE. Journal of information and communication convergence engineering 2022;20:212-8. https://doi.org/10.56977/jicce.2022.20.3.212

Image dehazing, as a common solution to weather-related degradation, holds great promise for photography, computer vision, and remote sensing applications. Diverse approaches have been proposed throughout decades of development, and deep-learning-based methods are currently predominant. Despite excellent performance, such computationally intensive methods as these recent advances amount to overkill, because image dehazing is solely a preprocessing step. In this paper, we utilize an autonomous image dehazing algorithm to analyze a non-deep dehazing approach. After that, we present a corresponding FPGA design for high-quality real-time vision systems. We also conduct extensive experiments to verify the efficacy of the proposed design across different facets. Finally, we introduce a method for synthesizing cloudy images (loosely referred to as hazy images) to facilitate future aerial surveillance research.

Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.

Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.

Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.

In recent years, machine vision algorithms have played an influential role as core technologies in several practical applications, such as surveillance, autonomous driving, and object recognition/localization. However, as almost all such algorithms are applicable to clear weather conditions, their performance is severely affected by any atmospheric turbidity. Several image visibility restoration algorithms have been proposed to address this issue, and they have proven to be a highly efficient solution. This paper proposes a novel method to recover clear images from degraded ones. To this end, the proposed algorithm uses a supervised machine learning-based technique to estimate the pixel-wise extinction coefficients of the transmission medium and a novel compensation scheme to rectify the post-dehazing false enlargement of white objects. Also, a corresponding hardware accelerator implemented on a Field Programmable Gate Array chip is in order for facilitating real-time processing, a critical requirement of practical camera-based systems. Experimental results on both synthetic and real image datasets verified the proposed method’s superiority over existing benchmark approaches. Furthermore, the hardware synthesis results revealed that the accelerator exhibits a processing rate of nearly 271.67 Mpixel/s, enabling it to process 4K videos at 30.7 frames per second in real time.