A novel unsupervised approach for multilevel image clustering from unordered image collection

A novel unsupervised approach to automatically constructing multilevel image clusters from unordered im- ages is proposed in this paper. The whole input image col- lection is represented as an imaging sample space （ISS） con- sisting of globally indexed image features extracted by a new efficient multi^view image feature matching method. By mak- ing an analogy between image capturing and observation of ISS, each image is represented as a binary sequence, in which each bit indicates the visibility of a corresponding feature. Based on information theory-inspired image popularity and dissimilarity measures, we show that the image content and distance can be quantitatively described, guided by which an input image collection is organized into multilevel clusters automatically. The effectiveness and the efficiency of the pro- posed approach are demonstrated using three real image col- lections and promising results were obtained from both qual- itative and quantitative evaluation.

A polynomial hybrid reflection model and measurement of its parameters based on images of sample

Reflectance model is a basic concept in computer vision. Some existing models combining the classical diffuse reflectance model and those for surfaces containing specular components can approximately describe real reflectance. But the ratio of diffuse and specular reflection decided manually has no clear meaning. We propose a new polynomial hybrid reflectance model. The reflectance map equation with a known shape （for example cylinder） as a sample is used to estimate parameters of the proposed reflectance model by least square regression algorithm. Then the reflectance parameters for surfaces of the same class of materials can be determined. Experiments are performed for a metal surface. The synthesis images produced by the proposed method and existing ones are compared with the real acquired image, and the results show that the proposed reflectance model is suitable for describing real reflectance.

A 10-bit column-parallel cyclic ADC for high-speed CMOS image sensors

This paper presents a high-speed column-parallel cyclic analog-to-digital converter（ADC） for a CMOS image sensor.A correlated double sampling（CDS） circuit is integrated in the ADC,which avoids a stand-alone CDS circuit block.An offset cancellation technique is also introduced,which reduces the column fixed-pattern noise（FPN） effectively.One single channel ADC with an area less than 0.02 mm^2 was implemented in a 0.13μm CMOS image sensor process.The resolution of the proposed ADC is 10-bit,and the conversion rate is 1.6 MS/s. The measured differential nonlinearity and integral nonlinearity are 0.89 LSB and 6.2 LSB together with CDS, respectively.The power consumption from 3.3 V supply is only 0.66 mW.An array of 48 10-bit column-parallel cyclic ADCs was integrated into an array of CMOS image sensor pixels.The measured results indicated that the ADC circuit is suitable for high-speed CMOS image sensors.

Design and performance of air flow-assisted ionization imaging mass spectrometry system

The imaging mass spectrometry（IMS） technology has experienced a rapid development in recent years.A new IMS technology which is based on air flow assisted ionization（AFAI） was reported.It allows for the convenient pretreatment of the samples and can image a large area of sample in a single measurement with high sensitivity.The AFAI in DESI mode was used as the ion source in this paper.The new IMS method is named AFADESI-IMS.The adoption of assisted air flow makes the sample pretreatment easy and convenient.An optimization of the distance between the ion transport tube and MS orifice increases the sensitivity of the system.For data processing,a program based on MATLAB with the function of numerical analysis was developed.A theoretical imaging resolution of a few hundred microns can be achieved.The composite AFAI-IMS images of different target analytes were imaged with high sensitivity.A typical AFAI-IMS image of the whole-body section of a rat was obtained in a single analytical measurement.The ability to image a large area for relevant samples in a single measurement with high sensitivity and repeatability is a significant advantage.The method has enormous potentials in the MS imaging of large and complicated samples.