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Bubble seed image filling is an important prerequisite for the image segmentation of flotation bubble that can be used to improve flotation automatic control. These common image filling algorithms in dealing with complex bubble image exists under-filling and over-filling problems. A new filling algorithm based on boundary point feature and scan lines (PFSL) is proposed in the paper. The filling algorithm describes these boundary points of image objects by means of chain codes. The features of each boundary point, including convex points, concave points, left points and right points, are defined by the point’s entrancing chain code and leaving chain code. The algorithm firstly finds out all double-matched boundary points based on the features of boundary points, and fill image objects by these double-matched boundary points on scan lines. Experimental results of bubble seed image filling show that under-filling and over-filling problem can be eliminated by the proposed algorithm. Keywords: Flotation bubble, Filling, Chain code, Point feature, Scan line

Lots of noises and heterogeneous objects with various sizes coexist in a complex image, such as an ore image; the classical image thresholding method cannot effectively distinguish between ores. To segment ore objects with various sizes simultaneously, two adaptive windows in the image were chosen for each pixel; the gray value of windows was calculated by Otsu’s threshold method. To extract the object skeleton, the definition principle of distance transformation templates was proposed. The ores linked together in a binary image were separated by distance transformation and gray reconstruction. The seed region of each object was picked up from the local maximum gray region of the reconstruction image. Starting from these seed regions, the watershed method was used to segment ore object effectively. The proposed algorithm marks and segments most objects from complex images precisely.

The InAs/GaAs quantum dots structure embedded in GaInP/Ga(In)As/Ge triple junction solar cell with and without Ga0.90In0.10As strain reducing layer was investigated. Conversion efficiency of 33.91% at 1,000 suns AM 1.5D with Ga0.90In0.10As strain reducing layer was demonstrated. A 1.19% improvement of the conversion efficiency was obtained via inserting the Ga0.90In0.10As strain reducing layer. The main contribution of this improvement was from the increase of the short-circuit current, which is caused by the reduction of the Shockley–Read–Hall recombination centers. Consequently, there was a decrease in open circuit voltage due to the lower thermal activation energy of confined carriers in Ga0.9In0.1As than GaAs and a reduction in the effective band gap of quantum dots.

In order to segment bubbles in floatation image, a segmentation method combined seed area with region growing is proposed. Firstly extracting seed region on the top of bubble according to image color feature, then formulating the seed boundary of each bubble, thirdly finding bubble boundary conditions. Each bubble grows from its seed boundary outwards and stops at its boundary. The method segments each image object rather than the whole image, the segmentation results is more accurate.

A new thresholding algorithm based on two neighbourhoods and Otsu's method is proposed. For each pixel, its two neighbourhoods are chosen by image and object sizes. The thresholds of neighbourhoods are optimised by Otsu's method, and then every pixel is binarised through the minimum of thresholds. Experimental results show that the proposed algorithm improves thresholding performance efficiently, especially for multi-object and uneven lighting images.