Contrast limited adaptive histogram equalization (CLAHE) and shadow removal for controlled environment plant production systems
Başlık çevirisi mevcut değil.
- Tez No: 403086
- Danışmanlar: DR. ALİ AKOĞLU
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Electrical and Electronics Engineering, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2013
- Dil: İngilizce
- Üniversite: The Unıversıty Of Arızona
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
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Özet (Çeviri)
Greenhouse interior climate presents a challenging environment to work with computer vision systems such as varying light intensities, shadows due to structural elements, and air flow induced canopy movement. In this study our objective is to minimize these effects by designing and developing histogram equalization and shadow removal methods optimized for operating in the greenhouse environment. Adaptive histogram equalization is a popular and effective method for enhancing the visibility of local details of an image. However, this method is computational intensive and has an inclination to amplify noise in relatively homogeneous regions of an image. The CLAHE solves these drawbacks, but requires large memory footprint which makes the approach not suitable for hardware implementation. We re-design the flow of the CLAHE and propose a new architecture that achieves real time processing of 640 × 480 images at a rate of 354.36 fps and reduces the hardware resource usage by a factor of 12X for block RAMs and 6.7X for logic blocks compared to state-of-the-art implementation. Our implementation reduces the execution time of the CLAHE by a factor of 21.6X with respect to the Matlab implementation. The presence of shadows in image makes it hard to detect and track object(s) of interest during non-contact sensing based plant health monitoring. State of the art shadow removal techniques perform best only under a specific angle of light that they are designed for. The shadow removal problem gets further complicated when the environmental conditions such as the effects of cloud and rain are taken into account. Detecting shaded region and increasing the illumination in shaded region without creating an artifact in the original image are two challenging problems from hardware implementation perspective. In our approach we consider converting image into YUV space in order to reduce the computation complexity and eliminate the need for double precision calculations. We reduce execution time of the shadow detection and recovery stages by a factor of 9.9X and 44.5X respectively for a 300 × 400 image with respect to the state of art method, without sacrificing the image quality.
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